WebGen-Agent

WebGen-Agent is an advanced website generation agent designed to autonomously create websites from natural language instructions. It was introduced in the paper WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning.

Code: https://github.com/mnluzimu/WebGen-Agent

Project Overview

WebGen-Agent combines state-of-the-art language models with specialized training techniques to create a powerful website generation tool. The agent can understand natural language instructions specifying appearance and functional requirements, iteratively generate website codebases, and refine them using visual and functional feedback.

Key Features

• Iterative Refinement: Continuously improves website appearance and functionality
• Feedback Integration: Uses both visual and functional feedback for enhanced performance
• Backtracking Mechanism: Reverts to previous states when encountering persistent errors
• Best Step Selection: Selects the optimal version based on visual and functional scores

How WebGen-Agent Works

WebGen-Agent follows an iterative, multi-step paradigm for website generation:

1. Code Generation: The agent generates code to create or edit website files based on natural language instructions
2. Code Execution: Dependencies are installed and the website service is started
3. Feedback Gathering:
   • A screenshot of the website is captured
   • A Visual Language Model (VLM) provides appearance feedback and scores
   • A GUI-agent tests the website functionality and provides functional feedback
4. Refinement: Based on the feedback, the agent continues to improve the website until it meets requirements

Step-GRPO with Screenshot and GUI-agent Feedback

The Step-GRPO with Screenshot and GUI-agent Feedback approach uses the screenshot and GUI-agent scores inherently produced in the WebGen-Agent workflow as step-level rewards:

• Screenshot Score: Quantifies the visual appeal and aesthetics of the website
• GUI-agent Score: Measures how well the website meets functional requirements

These dual rewards provide dense, reliable process supervision that significantly improves the model's ability to generate high-quality websites.

Sample Usage

To get started with WebGen-Agent, follow the installation steps and then try running inference.

Installation

1. Clone the repository:

   git clone https://github.com/mnluzimu/WebGen-Agent.git
   cd WebGen-Agent
   

2. Create a conda environment:

   conda create -n env/webgen-agent python=3.10
   conda activate env/webgen-agent
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Node.js and google-chrome are also necessary for the project. You can install them by running:

   bash src/scripts/install_node.sh
   bash src/scripts/install_chrome.sh
   

Running Inference

Before running inference, you should rename .env.template to .env and set the base urls and api keys for the agent-engine LLM and feedback VLM. They can be obtained from any openai-compatible providers such as openrouter, modelscope, bailian, and llmprovider.

You can also deploy open-source VLMs and LLMs by running src/scripts/deploy_qwenvl_32b.sh and src/scripts/deploy.sh. Scripts for single inference and batch inference can be found at src/scripts/infer_single.sh and src/scripts/infer_batch.sh.

Single Inference

python src/infer_single.py \
    --model deepseek-chat \
    --vlm_model Qwen/Qwen2.5-VL-32B-Instruct \
    --instruction "Please implement a wheel of fortune website." \
    --workspace-dir workspaces_root/test \
    --log-dir service_logs/test \
    --max-iter 20 \
    --overwrite \
    --error-limit 5

Batch Inference

python src/infer_batch.py \
    --model ${model_name} \
    --vlm_model Qwen/Qwen2.5-VL-32B-Instruct \
    --fb_model ${fb_model_name} \
    --data-path data/webgen-bench/test.jsonl \
    --workspace-root workspaces_root \
    --log-root service_logs \
    --max-iter 20 \
    --num-workers 4 \
    --eval-tag select_best \
    --error-limit 5 \
    --max-tokens -1 \
    --max-completion-tokens -1 \
    --temperature 0.5

WebGen-Bench Evaluation

The WebGen-Bench evaluation script is in run_ui_eval_with_answer.sh. For details of the WebGen-Bench dataset, see WebGen-Bench.

Resources

Links to the data and model parameters are as follows:

Citation

If you find our project useful, please cite:

@misc{lu2025webgenagentenhancinginteractivewebsite,
      title={WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning}, 
      author={Zimu Lu and Houxing Ren and Yunqiao Yang and Ke Wang and Zhuofan Zong and Junting Pan and Mingjie Zhan and Hongsheng Li},
      year={2025},
      eprint={2509.22644},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.22644}, 
}

@misc{lu2025webgenbenchevaluatingllmsgenerating,
      title={WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch}, 
      author={Zimu Lu and Yunqiao Yang and Houxing Ren and Haotian Hou and Han Xiao and Ke Wang and Weikang Shi and Aojun Zhou and Mingjie Zhan and Hongsheng Li},
      year={2025},
      eprint={2505.03733},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.03733}, 
}