Agent-EvalKit open-sources systematic evaluation for AI agent tools and paths

Most teams evaluate AI agents the same way they evaluate software: by checking outputs against expectations. But agents that autonomously choose tools and sequence operations across multiple sources can deliver seemingly correct responses while hallucinating over empty tool results or skipping verification steps. Agent-EvalKit closes this gap by tracing the agent’s entire execution path—which tools were called, what data those tools returned, and whether the response faithfully reflects that data. It is an open-source toolkit (Apache 2.0) that integrates directly with AI coding assistants such as Claude Code, Kiro CLI, and Kilo Code. Instead of treating evaluation as a separate post-deployment effort, the toolkit brings it into the development environment. Developers describe evaluation goals in natural language, and Agent-EvalKit reads the agent’s source code, generates targeted test cases, runs evaluations, and produces a final report with specific, code-level improvement recommendations referencing exact locations in the codebase.

The toolkit evaluates agents across multiple quality dimensions that no single metric captures: whether responses are grounded in actual tool results, whether the right tools were called with correct parameters, and whether the output is coherent and useful. It combines code-based evaluators (fast and reproducible) with LLM-as-judge evaluators (nuanced but more expensive). Developers drive the workflow via slash commands like /evalkit.plan and /evalkit.data, appending natural language guidance about the quality dimensions that matter most for their agent. Every artifact—from the evaluation plan linking to code analysis through the final report—builds on a detailed model of the agent’s tools, system prompt, and framework configuration. By making rigorous evaluation infrastructure accessible to teams that cannot build it from scratch, Agent-EvalKit transforms agent testing from a guessing game into a systematic, actionable process.