Releases·Tool Release
Where should the LLM sit in a scraping pipeline? Minexa argues on top of extraction, not as the extractor
A Reddit post maps where LLMs fit in scraping pipelines for RAG, lead lists, and market research. The author argues LLM-as-extractor suffers at volume — context limits, per-page cost, plausible-but-wrong field fills — and works better layered on top of deterministic DOM-based extraction. The post promotes Minexa.ai's API, trained via a Chrome extension, as one option for that slot.
1 engagement1 sourceFri, Oct 2, 2026, 01:19 PM
AI Overview
- The classic pipeline is request/JS render, DOM parse, CSS/XPath field pull, clean and store — steps 1 and 3 break most from anti-bot layers, CAPTCHAs and layout changes
- LLM-as-extractor avoids selectors but hits context limits on long pages, scales cost with page size, fabricates plausible values for missing fields, and fumbles merged or nested tables
- Practices that hold either way: exponential-backoff retries for 5xx, raw HTML snapshots for re-runs, deterministic regex/enum validation, schema-mismatch alerts, cron freshness
- Minexa.ai trains scrapers in a Chrome extension, runs deterministic DOM-based extraction, returns null for missing values, and accepts passed-in HTML via file_urls; nested fields return as unsorted object lists
Related
Social·Community
Why I combined fetching and extraction in one agent tool
A developer building a lead-generation agent with the OpenAI Agents SDK and ZenRows found that exposing fetching and extraction as separate tools overflowed model context with 540K-character pages. Combining fetch+extract and enrich+score into single tools keeps large intermediates in Python and made the pipeline faster and more robust. The writeup raises a design question for multi-step agents: how to divide control between model orchestration and deterministic code.
A developer building a lead-generation agent with the OpenAI Agents SDK and ZenRows found that exposing fetching and extraction as separate tools overflowed model context with 540K-character pages. Combining fetch+extract and enrich+score into single tools keeps large intermediates in Python and made the pipeline faster and more robust. The writeup raises a design question for multi-step agents: how to divide control between model orchestration and deterministic code.
17 engagement2 sourcesredditTue, Oct 6, 2026, 07:27 AM
Social·Community
web scraping for langchain agents: DIY (playwright/bs4) vs managed APIs (contextdev, firecrawl), my experience
A Reddit user shares hands-on experience building web-scraping tools for LangChain agents, comparing DIY Playwright/BeautifulSoup stacks with managed APIs like Firecrawl and context.dev. The writeup finds DIY viable for fixed workflows but costly and maintenance-heavy for open-ended agent web search, where managed APIs' built-in rendering, proxy rotation, and LLM-ready markdown outweigh per-request costs.
A Reddit user shares hands-on experience building web-scraping tools for LangChain agents, comparing DIY Playwright/BeautifulSoup stacks with managed APIs like Firecrawl and context.dev. The writeup finds DIY viable for fixed workflows but costly and maintenance-heavy for open-ended agent web search, where managed APIs' built-in rendering, proxy rotation, and LLM-ready markdown outweigh per-request costs.
27 engagement1 sourceredditSun, Sep 13, 2026, 08:33 PM
Social·Community
Developer reports typed per-site tools beat generic scrapers for agent data extraction
A developer describes how giving an agent a typed tool per website outperformed a generic scraper for pulling structured data. The generic approach repeatedly failed by re-deriving page structure on every call, silently returning garbage after site changes, and making debugging difficult. The fix was to derive an extraction schema once with an LLM, cache it, and expose each site as a typed tool.
A developer describes how giving an agent a typed tool per website outperformed a generic scraper for pulling structured data. The generic approach repeatedly failed by re-deriving page structure on every call, silently returning garbage after site changes, and making debugging difficult. The fix was to derive an extraction schema once with an LLM, cache it, and expose each site as a typed tool.
13 engagement2 sourcesredditFri, Aug 21, 2026, 02:21 PM
Products·building
LLM-based e-commerce site scraper
A method that uses LLMs to extract product data from e-commerce websites, replacing brittle CSS selectors with natural language understanding. It aims to simplify scraping for developers who struggle with dynamic site structures.
A method that uses LLMs to extract product data from e-commerce websites, replacing brittle CSS selectors with natural language understanding. It aims to simplify scraping for developers who struggle with dynamic site structures.
0 engagement1 sourcerssWed, Jul 29, 2026, 09:12 AM
Social·Community
Developer shares method to reduce RAG hallucinations by cleaning HTML before embedding
A developer on Reddit reports that feeding raw HTML into LangChain's RAG pipelines causes token waste, poor embeddings, and hallucinations. They advocate converting HTML to clean Markdown before splitting, improving cost-efficiency and retrieval quality.
A developer on Reddit reports that feeding raw HTML into LangChain's RAG pipelines causes token waste, poor embeddings, and hallucinations. They advocate converting HTML to clean Markdown before splitting, improving cost-efficiency and retrieval quality.
0 engagement1 sourceredditSat, Jul 18, 2026, 10:19 AM