Enables LLM agents to run self-healing JMeter performance tests via natural language, from environment setup to capacity discovery and SLO verdict reporting.
Enables AI assistants to programmatically create, execute, and analyze Apache JMeter performance tests. It supports automated bottleneck detection, report generation, and distributed testing management through natural language.
A Model Context Protocol (MCP) server implementation that allows AI assistants to run k6 load tests through natural language commands, supporting custom test durations and virtual users.
Integrates Apache JMeter with AI assistants to run and manage load tests through natural language. It enables users to execute test plans, parse results, inspect test structures, and compare performance metrics across different runs.
Converts non-functional requirements and code into secure, traceable JMeter performance tests with coverage analysis, plan generation, execution, and GitHub draft PRs.