• 🤖 AI-Powered Testing: Performs autonomous website testing—explores pages, plans actions, and executes end-to-end flows without manual scripting.
• 📊 Multi-Dimensional Observation: Covers functionality, performance, user experience, and basic security; evaluates load speed, design details, and links to surface issues.
• 🎯 Actionable Recommendations: Runs in real browsers and provides concrete suggestions for improvement.
• 📈 Visual Reports: Generates detailed HTML test reports with clear, multi-dimensional views for analysis and tracking.

• 🤖 Conversational UI: Autonomously plans goals and interacts across a dynamic chat interface
• 🎨 Creative Page: Explores page structure, identifies elements

Try Demo: 🤗Hugging Face · 🚀ModelScope

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

Recommended versions: Docker >= 24.0, Docker Compose >= 2.32.

# 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

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 Analysis - Lighthouse (Optional)

# Requires Node.js >= 18.0.0 npm install

Security Scanning - Nuclei (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

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 dynamic_step_generation: # Optional, configuration for dynamic steps generation enabled: True # Optional, default False, recommended to set True to enable dynamic step generation max_dynamic_steps: 5 # Optional, default 5 test steps generated per trigger min_elements_threshold: 2 # Optional, default trigger threshold is 2 DOM element differences ux_test: # User experience testing enabled: True performance_test: # Performance analysis enabled: False security_test: # Security scanning enabled: False llm_config: # Vision model configuration, currently supports OpenAI SDK compatible format only model: gpt-4.1-2025-04-14 # 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:

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

• Default Mode: The default mode focuses on whether UI interactions (e.g., clicks and navigations) complete successfully.

UX (User Experience) testing focuses on usability, and user-friendliness. The model output in the results provides suggestions based on best practices to guide optimization.

Based on our testing, these models work well with WebQA Agent:

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

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 item visualization and local service real-time reasoning process display
4. Capability expansion: Multi-model integration and more evaluation dimensions

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

This project is licensed under the Apache 2.0 License.