What is Feather Wand Agent

Feather Wand Agent is a comprehensive AI-powered toolkit designed for performance testing and monitoring. It integrates multiple industry-standard performance testing tools such as JMeter, k6, Gatling, and Locust into a single interface, enabling users to execute and analyze performance tests using natural language.

Use cases

Use cases for Feather Wand Agent include executing performance tests on web applications, analyzing system performance under load, conducting unit tests for reliability, and monitoring test executions to ensure optimal performance.

How to use

To use Feather Wand Agent, start by cloning the repository and installing the necessary dependencies. Configure the environment variables as needed, and then launch the agent web interface using the command ‘adk web’. This will allow you to interact with the agent at http://localhost:8000.

Key features

Key features of Feather Wand Agent include a multi-tool agent framework supporting JMeter, k6, Gatling, and Locust, an AI-powered conversational interface for executing tests, unit testing utilities for reliability, performance metrics collection and analysis, monitoring capabilities for test execution, and flexible environment variable configuration.

Where to use

Feather Wand Agent can be used in various fields that require performance testing and monitoring, including software development, web application testing, and system performance evaluation.

🚀 Feather Wand Agent

📝 Description

Feather Wand Agent is a comprehensive AI-powered toolkit for performance testing and monitoring. It integrates multiple industry-standard performance testing tools (JMeter, k6, Gatling, and Locust) into a single, unified interface, allowing users to execute and analyze performance tests through natural language interactions.

✨ Features

• 🏗️ Multi-tool agent framework supporting JMeter, k6, Gatling, and Locust
• 🤖 AI-powered conversational interface for executing performance tests
• 🧪 Unit testing utilities for ensuring agent reliability
• 📊 Performance metrics collection and analysis
• 🕵️‍♂️ Monitoring capabilities for test execution
• 🔄 Environment variable configuration for flexible deployment

🛠️ Prerequisites

• Python 3.8+
• JMeter (for JMeter tests)
• k6 (for k6 tests)
• Maven or Gradle (for Gatling tests)
• Locust (for Locust tests)

📋 Installation

0. Get a Google API key from either:

• https://console.developers.google.com/ (for Google Cloud)
• https://aistudio.google.com (for Google AI Studio)

Save this API key in the .env file, which will be created in step 3 of the installation process

# If using Gemini via Google AI Studio
GOOGLE_GENAI_USE_VERTEXAI="False"
GOOGLE_API_KEY="<YOUR_GOOGLE_API_KEY>"

# # If using Gemini via Vertex AI on Google Cloud
# GOOGLE_CLOUD_PROJECT="your-project-id"
# GOOGLE_CLOUD_LOCATION="your-location" #e.g. us-central1
# GOOGLE_GENAI_USE_VERTEXAI="True"

1. Clone the repository:

git clone https://github.com/yourusername/perf_tools_google_agent.git
cd perf_tools_google_agent

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment variables (copy .env.example to .env and modify as needed):

cp .env.example .env

🏃‍♂️ Usage

Starting the Agent

Launch the agent web interface:

adk web

This will start the agent at http://localhost:8000, where you can interact with it through the chat interface.

Supported Performance Testing Tools

JMeter

The agent can execute JMeter test plans (.jmx files) in both GUI and non-GUI modes with customizable duration and virtual user count.

Example commands:

• Run in non-GUI mode (default):

  Run my JMeter test at /path/to/test.jmx
  

• Run in non-GUI mode (with custom settings):

  Run my JMeter test at /path/to/test.jmx with 20 users and 300 seconds
  

• Run in GUI mode:

  Open JMeter GUI with my test plan at /path/to/test.jmx
  

k6

The agent can execute k6 scripts (.js files) with customizable duration and virtual user count.

Example commands:

• Run with default settings (30s duration, 10 VUs):

  Execute k6 script at /path/to/script.js
  

• Run with custom settings:

  Run k6 test at /path/to/script.js with 50 users for 2 minutes
  

Locust

The agent can execute Locust test files (.py files) with configurable parameters.

Example commands:

• Run with default settings:

  Run Locust test at /path/to/test.py
  

• Run with custom settings:

  Execute Locust test at /path/to/test.py against http://example.com with 200 users at spawn rate 20
  

Gatling

The agent can execute Gatling simulations using either Maven or Gradle as the runner.

Example commands:

• Run a Gatling simulation:

  Run Gatling test in directory /path/to/gatling/project
  

• Run a specific simulation class:

  Execute Gatling simulation MySimulation in directory /path/to/gatling/project
  

🔧 Configuration

The agent can be configured through environment variables in the .env file:

General Configuration

• FEATHERWAND_NAME: Name of the agent (default: featherwand_agent)
• FEATHERWAND_MODEL: AI model to use (default: gemini-2.0-flash-exp)
• FEATHERWAND_DESCRIPTION: Description of the agent

JMeter Configuration

• JMETER_BIN: Path to JMeter binary (default: jmeter)
• JMETER_JAVA_OPTS: Java options for JMeter

k6 Configuration

• K6_BIN: Path to k6 binary (default: k6)

Locust Configuration

• LOCUST_BIN: Path to Locust binary (default: locust)
• LOCUST_HOST: Default host to test (default: http://localhost:8089)
• LOCUST_USERS: Default number of users (default: 100)
• LOCUST_SPAWN_RATE: Default spawn rate (default: 10)
• LOCUST_RUNTIME: Default runtime (default: 30s)
• LOCUST_HEADLESS: Whether to run in headless mode (default: true)

Gatling Configuration

• GATLING_RUNNER: Runner to use for Gatling (default: mvn, alternative: gradle)

📁 Project Structure

perf_tools_google_agent/
├── .env.example          # Example environment variables
├── .gitignore            # Git ignore file
├── README.md             # This file
├── requirements.txt      # Python dependencies
├── multi_tool_agent/     # Main agent code
│   ├── __init__.py       # Package initialization
│   ├── agent.py          # Agent definition and tools
│   ├── jmeter_utils.py   # JMeter utilities
│   ├── k6_utils.py       # k6 utilities
│   ├── locust_utils.py   # Locust utilities
│   ├── gatling_utils.py  # Gatling utilities
│   ├── prompt.py         # Agent prompts
│   ├── sample/           # Sample test files
│   └── tests/            # Unit tests

🧪 Testing

pytest tests/unit/

🤝 Contributing

Contributions are welcome! Please ensure tests pass before submitting pull requests.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📜 License

MIT