Generates and maintains structured YAML evaluation files for testing AI agent performance and response accuracy.
Streamlines the process of building robust testing frameworks for AI agents by automating the creation of AgentV evaluation files. It enables developers to define complex test cases with multi-role conversation threads, integrate file-based inputs, and configure sophisticated validation logic using either programmatic code scripts or LLM-based judges. This skill ensures consistency across agent benchmarks and helps optimize performance through systematic, schema-validated evaluation workflows that support sequential evaluator chaining.
主要功能
01Schema-validated YAML generation for structured agent test cases
02Configuration of LLM judges for qualitative response assessment
03Support for multi-role conversation threading including system, user, assistant, and tool roles
04Sequential evaluator chaining for multi-stage testing workflows
05Integration of custom code-based evaluators for programmatic validation
064 GitHub stars
使用场景
01Creating regression tests for agent workflows using real-world file inputs and expected outcomes
02Implementing automated quality gates that combine programmatic unit tests with LLM-based reasoning
03Benchmarking a new AI agent's performance against specific coding or reasoning tasks
