WebQA Agent

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WebQA Agent is an autonomous web agent that audits performance, functionality, and UX for any web product.

🚀 Core Features

🧭 Overview

📋 Feature Highlights

• 🤖 AI-Powered Testing: WebQA Agent autonomously conducts website testing, from page crawling and test case generation to execution, achieving end-to-end functional test automation.
• 📊 Multi-Dimensional Test: Covers core testing scenarios, including functionality, performance, user experience, and security, evaluating page load speed, design details, and links for comprehensive system quality assurance.
• 🎯 Precise Diagnostics: Performs deep testing in real browser environments and provides actionable optimization recommendations.
• 📈 Visual Reports: Generates detailed HTML test reports with a multi-dimensional visual presentation of results for easy analysis and tracking.

📌 Test Cases

 

Left: AI Functional Testing　|　Right: Multiple Test Scenarios Coverage

Installation & Configuration

🚀 One-Click Docker Setup

Before starting, ensure Docker is installed. If not, please refer to the official installation guide: Docker Installation Guide.

# 1. Download configuration template
mkdir -p config && curl -fsSL https://raw.githubusercontent.com/MigoXLab/webqa-agent/main/config/config.yaml.example -o config/config.yaml

# 2. Edit configuration file
# Set target.url, llm_config.api_key and other parameters

# 3. One-click start
curl -fsSL https://raw.githubusercontent.com/MigoXLab/webqa-agent/main/start.sh | bash

Source Installation

git clone https://github.com/MigoXLab/webqa-agent.git
cd webqa-agent

Install Python >= 3.10 and run the following commands:

pip install -r requirements.txt
playwright install

Performance Testing - Lighthouse Installation (Optional)

# Requires Node.js >= 18.0.0
npm install

Security Testing - Nuclei Installation (Optional)

Download from: Nuclei Releases

# MacOS
brew install nuclei

# For other systems, download the appropriate version from the link above

# Update templates and verify installation
nuclei -ut -v          # Update Nuclei templates
nuclei -version        # Verify successful installation

After configuring config/config.yaml (refer to "Usage > Test Configuration"), run:

python webqa-agent.py

Online Demo

Experience online: WebQA-Agent on ModelScope

Usage

Test Configuration

webqa-agent uses YAML configuration for test parameters:

target:
  url: https://example.com/                       # Website URL to test
  description: example description

test_config:                                      # Test configuration
  function_test:                                  # Functional testing
    enabled: True
    type: ai                                      # default or ai
    business_objectives: example business objectives  # Recommended to include test scope, e.g., test search functionality
  ux_test:                                        # User experience testing
    enabled: True
  performance_test:                               # Performance testing
    enabled: False
  security_test:                                  # Security testing
    enabled: False

llm_config:                                       # Vision model configuration, currently supports OpenAI SDK compatible format only
  model: gpt-4.1                                  # Recommended
  api_key: your_api_key
  base_url: https://api.example.com/v1

browser_config:
  viewport: {"width": 1280, "height": 720}
  headless: False                                 # Automatically overridden to True in Docker environment
  language: zh-CN
  cookies: []

Please note the following important considerations when configuring and running tests:

1. Functional Testing Notes

• AI Mode: When specifying the number of test cases to generate in the configuration file, the system may re-plan based on based on actual testing conditions. This may result in the final number of executed test cases differing from the initial configuration to ensure testing accuracy and effectiveness.

• Default Mode: The default mode of functional testing primarily verifies whether UI element clicks execute successfully, including basic interactive functions like button clicks and link navigation.

2. User Experience Testing Notes

UX (User Experience) testing focuses on evaluating website interaction design, usability, and user-friendliness. The model output in the test results provides suggestions for improvement suggestions based on user experience best practices to guide development and design teams in optimization.

View Results

Test results will be generated in the reports directory. Open the HTML report within the generated folder to view results.

Roadmap

1. Continuous optimization of AI functional testing: Improve coverage and accuracy
2. Functional traversal and page validation: Verify business logic correctness and data integrity
3. Interaction and visualization: Test case visualization and local service real-time reasoning process display
4. Capability expansion: Multi-model integration and more evaluation dimensions

Acknowledgements

• natbot: Drive a browser with GPT-3
• Midscene.js: AI Operator for Web, Android, Automation & Testing
• browser-use: AI Agent for Browser control

Open Source License

This project is licensed under the Apache 2.0 License.