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RAG & Knowledge Systems

LLM Reader

LLM Reader is an open-source Python library that converts webpage HTML into text intended for language-model input. Its documented helpers can retrieve page source and process it, with the project describing support for extracting page content, website and image links, and tables.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

LLM Reader — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a workflow already has a webpage URL or HTML source and needs to prepare that page as model input before a separate extraction or knowledge-ingestion step.

Who it is for

  • Python developers building web-scraping pipelines
  • Teams preparing retrieved webpages for RAG knowledge ingestion
  • Developers who want page text and links ready for a later LLM prompt

Documented uses

  • Convert webpage HTML into text suitable for an LLM prompt.
  • Preserve useful page details such as links, image links and tables for downstream extraction.
  • Prepare retrieved webpage content for scraping or RAG ingestion.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the MIT license.

  • The library can retrieve page source itself, or process HTML source obtained through other code or services.
  • The documented processing interface is asynchronous in its example.
  • Playwright browser and system dependencies are listed for the current scraping setup.
  • The example leaves prompt construction, model choice, and text truncation to the developer.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install LLM Reader and prepare a page

Install the library and use its helpers to turn a webpage’s HTML source into processed text.

Before you start

  • Python environment with pip
  • Playwright browser and system dependencies
  • A webpage URL

Steps

  1. Install the library from its GitHub repository with pip.

  2. Install Playwright’s browser and dependencies using the commands in the README.

  3. Import get_page_source and get_processed_text from the documented modules.

  4. Retrieve the page source, pass it with the URL to get_processed_text, and print or otherwise use the returned text.

Expected outcome

Processed webpage text ready to place in an LLM prompt or a downstream scraping or RAG workflow.

Guide limitations

  • The README’s example uses asynchronous calls; adapt the surrounding code to your application.
  • The example recommends truncating processed text to fit the model context, as needed.
  • This prepares input text; extraction by an LLM is a separate downstream step.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
12 October 2026
Original website title
m92vyas/llm-reader · Official homepage title · retained in its source language
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