GitHub Wiki & Markdown Repository Scraper

Created by

LERNOWA AI LABS PRIVATE LIMITED

Actor

RAG Documentation Scraper — LLM-Ready Chunker

Automatically scrape GitHub wikis and markdown repositories into clean, structured data files optimized for RAG ingestion.

Try for free

RAG Documentation Scraper — LLM-Ready Chunkerlernowa_ai_labs/rag-docs-scraper

Content

Metadata

Input

Start URLs(required)

url:https://github.com/apify/crawlee/wiki

URL Path Patterns:/apify/crawlee/wiki/**

Max Crawl Depth:2

Max Pages Per Run:200

Chunking Strategy:heading_boundary

Chunk Size (tokens):512

Chunk Overlap (tokens):50

Output Format:markdown

Chunk Size (tokens) — Quick Override:500

Extra CSS Selectors to Exclude:.wiki-rightbar+2

Deduplicate Chunks:true

PDF Handling:skip

AWS Region:us-east-1

Enable Incremental (Delta) Crawling:false

DynamoDB Table Name:rag-docs-scraper-delta

Use Apify Proxy:false

Max Concurrency:5

Navigation Timeout (s):30

Output fields

Content

Metadata

How it works

Sign up on Apify or log in

The RAG Documentation Scraper — LLM-Ready Chunker Actor is ready to run with the inputs already set for "GitHub Wiki & Markdown Repository Scraper". Sign up or log in and you'll land in Apify Console with a copy of this exact task.

Start the "GitHub Wiki & Markdown Repository Scraper" task

The RAG Documentation Scraper — LLM-Ready Chunker Actor will start running based on the input automatically. You can also change all the input fields, like start urls, URL Path Patterns, and more, to match your specific use case.

Receive the "GitHub Wiki & Markdown Repository Scraper" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as content and metadata.

Integrate into your workflow

The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "GitHub Wiki & Markdown Repository Scraper" task via API.