<a href="https://arxiv.org/pdf/2404.12753.pdf"><img src="https://github.com/EZ-hwh/AutoScraper/raw/main/assets/Paper-Arxiv-orange.svg" ></a>
<a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FEZ-hwh%2FAutoCrawler&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false"/></a>
This is the official code for paper "AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation".

# Clone the AutoScraper repository
git clone https://github.com/EZ-hwh/AutoCrawler
# Change directory into the cloned repository
cd AutoCrawler
# Optional: Create a Conda environment for AutoScraper
# conda create -n autocrawler python=3.9
# conda activate autocrawler
# Install required dependencies
pip install -r requirements.txt
If you want to reproduce the result we report in paper.
# Generate scraper with AutoScraper
python crawler_generation.py \
--pattern reflexion \
--dataset swde \
--model ChatGPT \
--seed_website 3 \
--save_name ChatGPT \
--overwrite False
# Extract information with scraper
python crawler_extraction.py \
--pattern autocrawler \
--dataset swde \
--model GPT4
# Evaluate the extraction on SWDE dataset
python run_swde/evaluate.py \
--model GPT4 \
--pattern autocrawler
If you find this work useful, please consider citing our work:
@misc{huang2024autoscraperprogressiveunderstandingweb,
title={AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation},
author={Wenhao Huang and Zhouhong Gu and Chenghao Peng and Zhixu Li and Jiaqing Liang and Yanghua Xiao and Liqian Wen and Zulong Chen},
year={2024},
eprint={2404.12753},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2404.12753},
}
$ claude mcp add AutoScraper \
-- python -m otcore.mcp_server <graph>