🚀 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.

Example commands:

• Run in non-GUI mode (default):

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

• 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