hidai25/eval-view
🔌 MCP Serverhidai25
A regression testing framework for AI agents to detect behavioral drift and ensure consistent performance in CI pipelines.
eval-view addresses the critical challenge of non-deterministic behavior in AI agents by providing a structured approach to regression testing. The tool enables developers to capture 'golden baselines'—verified, high-quality outputs—that serve as the benchmark for future iterations. During the development lifecycle, eval-view compares new agent outputs against these baselines to identify subtle behavioral drifts that traditional unit tests might miss.
Key features include native MCP (Model Context Protocol) support, allowing for deep integration with modern AI development environments. It is designed to be highly interoperable, functioning effectively with popular frameworks like LangGraph and CrewAI, as well as any custom agent architecture that communicates via HTTP. By automating the validation process, eval-view acts as a quality gate in CI/CD pipelines, ensuring that updates to prompts, models, or logic do not degrade the agent's core capabilities. This focus on observability and automated verification is essential for teams moving AI agents from experimental prototypes to robust, production-grade systems.
💡Highlights
- ├─Regression testing for AI agents
- ├─Golden baseline output tracking
- └─Framework-agnostic HTTP support
🎯For
- ├─AI Engineers
- ├─DevOps Engineers
- └─QA Automation Engineers