llm-reader

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Basic Information

This repository provides an open source utility to convert webpages into LLM-friendly input text for use in retrieval-augmented generation and other LLM workflows. It is presented as an alternative to proprietary reader APIs and crawler services and is intended for developers who need a preprocessing step that turns a URL or web document into cleaner, LLM-ready text. The README shows simple usage via an asynchronous function that takes a URL and returns prepared text. The project notes it does not include anti-blocking or advanced parsing for non-HTML formats and recommends a paid API service named ParseExtract for anti-blocking, PDF/DOCX/image parsing, OCR, and table extraction when those capabilities are required. The repository also links to a companion scraping project for broader scraping and search workflows.

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App Details

Features
Converts any webpage URL into text formatted and preprocessed for direct LLM consumption. Provides an asynchronous helper function named url_to_llm_text demonstrated in the README. Packaged for easy installation from the repository with a pip install command. Focuses on extracting webpage content and associated links such as image and site links that are commonly needed for scraping tasks. Advertised as an open source alternative to reader and crawl APIs. Includes a documentation wiki for usage details and points users to an external paid API when anti-blocking, OCR, PDF/DOCX parsing, or table extraction is needed. Works as a preprocessing building block to fit into larger scraping or RAG pipelines.
Use Cases
It improves the quality and reliability of inputs fed to language models by preprocessing webpages into cleaner, LLM-ready text, which helps with extraction accuracy and downstream RAG tasks. By extracting site and image links it supports common scraping needs such as e-commerce data collection. The async interface and pip-installable package make it easy to integrate into developer workflows and automation pipelines. For users who need robust crawling without blocking or support for PDFs and images, the README recommends combining this tool with a specialized parsing/anti-blocking service. The project is useful as a lightweight, open source preprocessing component in broader web scraping and LLM ingestion architectures.

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