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Open-source web crawler and scraper for LLMs and AI agents: any website into clean, LLM-ready Markdown. Run it yourself, or use Crawl4AI Cloud with one key. Join our Discord https://discord.gg/VuxzM8mtf

Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

[SIGMOD'27] Easy Data Preparation with latest LLMs-based Operators and Pipelines.

  • data
  • data-cleaning
  • data-pipelines
  • data-processing

Python & Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML

A Python utility / library to sort imports.

  • auto-formatter
  • isort
  • sorting-imports
  • python-utility

Read and analyze SEC EDGAR filings in Python. 10-K, 8-K, XBRL financials, Form 3/4/5, 13F, ADV — clean API, well-typed, MIT-licensed.

  • 10-k
  • 10-q
  • balance-sheet
  • edgar

A command-line tool that helps you clean up Git branches that have been merged into master.

Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

Clean PyTorch implementations of imitation and reward learning algorithms

  • reward-learning
  • inverse-reinforcement-learning
  • imitation-learning
  • gymnasium

:trophy: Show some love to your contributors! A widget for your repo README. Visual and clean. Refreshes every hour.

  • readme
  • contributors
  • readme-badges
  • social-buttons

Parse human names into title, given, middle, family, suffix, nickname and maiden name. Typed, zero-dependency, CJK-aware.

  • python
  • text-processing
  • text-parser
  • python-module

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.