WikiAutoGen - Automated Wikipedia Content Generation System

Zhongyu Yang^{1, 2*}, Jun Chen^1*, Dannong Xu^1,3, Junjie Fei¹, Xiaoqian Shen¹, Liangbing Zhao¹, Chun-Mei Feng⁴, Mohamed Elhoseiny¹

¹King Abdullah University of Science and Technology, ²Lanzhou University, ³The University of Sydney, ⁴IHPC, A*STAR

👀 The code is coming soon...

Installation

git clone https://github.com/wikiautogen/wikiautogen_code.git
cd wikiautogen
conda create -n wikiautogen python=3.11
conda activate wikiautogen
pip install -r requirements.txt

Key Features

• 🖼️ Multimodal Content Generation with image-aware topic proposal
• 🤖 Automated Research using search engines (Serper/You.com)
• 📝 Structured Writing with outline generation and article polishing
• 🔍 Fact Verification through multi-perspective conversation simulation

Quick Start

1. Environment Setup

export OPENAI_API_KEY="your_openai_key"
export SERPER_API_KEY="your_serper_key"

2. Process Topics with Images

from src import RunnerArguments, Runner, WikiLMConfigs
from src.lm import OpenAIModel
from src.rm import SerperRM
from src.wikiautogen.modules.outline_proposal import WikipediaProposalGenerator

generator = WikipediaProposalGenerator(
    openai_api_key=os.getenv("OPENAI_API_KEY"),
    serper_api_key=os.getenv("SERPER_API_KEY")
)

lm_configs = WikiLMConfigs()
gpt_4o_mini = OpenAIModel(
    model='gpt-4o-mini',
    temperature=0.9,
    api_key=os.getenv("OPENAI_API_KEY")
)
lm_configs.set_all_models(gpt_4o_mini)

runner = Runner(
    RunnerArguments(
        output_dir="./output",
        max_conv_turn=3,
        max_perspective=3,
        search_top_k=10
    ),
    lm_configs,
    SerperRM(
        serper_search_api_key=os.getenv('SERPER_API_KEY'),
        query_params={"num": 10}
    )
)

topic = input('Topic: ')
img_url = input('Image: ')

new_topic, proposal = generator.generate_proposal(img_url, topic)

runner.run(
    og_topic=topic,
    topic=new_topic,
    proposal=proposal,
    do_research=True,
    do_generate_outline=True,
    do_generate_article=True,
    do_polish_article=True
)


runner.mmrun(
    og_topic=topic,
    topic=new_topic,
    proposal=proposal,
    do_positing=True,
    do_Retrieve_images=True,
    do_mmpolish=True
)

• do_research: if True, simulate conversations with difference perspectives to collect information about the topic; otherwise, load the results.
• do_generate_outline: if True, generate an outline for the topic; otherwise, load the results.
• do_generate_article: if True, generate an article for the topic based on the outline and the collected information; otherwise, load the results.
• do_polish_article: if True, polish the article by adding a summarization section and (optionally) removing duplicate content; otherwise, load the results.
• do_positing: if True, generate a positioning proposal for the article; otherwise, load the results.
• do_Retrieve_images: if True, generate a multimodal article for the topic based on the positioning proposal and the collected information; otherwise, load the results.
• do_mmpolish: if True, polish the article by enhancing coherence and consistency across modalities, focusing on potential discrepancies between textual content and visual figures.

License

This project is licensed under the MIT License. Content generation based on Wikipedia data follows CC BY-SA guidelines.

Citation

@misc{yang2025wikiautogenmultimodalwikipediastylearticle,
      title={WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation}, 
      author={Zhongyu Yang and Jun Chen and Dannong Xu and Junjie Fei and Xiaoqian Shen and Liangbing Zhao and Chun-Mei Feng and Mohamed Elhoseiny},
      year={2025},
      eprint={2503.19065},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.19065}, 
}

👍 Acknowledgement

WikiAutoGen is built with reference to the following outstanding works: Storm, Co-storm, Dspy. Thanks！