Evaluate AI Agents Systematically with Agent-EvalKit

Research Context and Background

Building AI agents is becoming more common, but testing them is difficult. Traditional software testing checks if the final answer is correct. However, AI agents work differently. They choose their own tools and run steps in a specific order. An agent might give a perfect-looking answer while using fake facts or skipping important checks. These hidden problems are hard to find with normal testing methods.

Agent-EvalKit is an open-source toolkit designed to fix this problem. It helps teams test AI agents by looking at their entire process, not just the final result. The tool works directly with AI coding assistants like Claude Code. It guides developers through a six-step process to plan tests, run them, and find specific code fixes. This approach turns vague concerns about reliability into clear actions for improving the software.

Benefits

Agent-EvalKit offers several key advantages for teams building AI agents. First, it provides a complete view of agent behavior. It checks if the agent uses the right tools, if the data it uses is real, and if the final output makes sense. Second, it saves time and effort. Instead of building complex testing systems from scratch, teams can use this ready-made toolkit. Third, it gives actionable advice. The tool does not just show scores. It points developers to the exact lines of code that need fixing. Finally, it supports continuous improvement. Teams can run these tests automatically as part of their development workflow to catch errors early.

Use Cases

This tool is ideal for teams developing autonomous AI agents that interact with multiple data sources. For example, a travel research agent that searches for flights and hotels needs to be very accurate. Agent-EvalKit can test if the agent finds real prices or if it makes up numbers. It is also useful for teams using frameworks like LangGraph, CrewAI, or Strands. Developers can integrate the tool into their CI/CD pipelines. This means every time a developer saves code, the system automatically checks for quality issues. It helps ensure that new features do not break existing functionality before the software goes live.

Pricing

Agent-EvalKit is available as open-source software under the Apache 2.0 license. This means it is free to use for both personal and commercial projects. However, using some of its advanced features, such as LLM-as-judge metrics, requires an active AWS account with Amazon Bedrock enabled. The tool itself does not charge a fee, but users must have their own cloud infrastructure to run the necessary models.

Vibes

The community response to Agent-EvalKit has been positive, especially among developers working with AI agents. In a real-world study, a team used the tool to evaluate a travel research agent. They found that while the agent gave clear advice, it was making up facts about exchange rates and temperatures. The tool helped them identify this issue quickly. The report generated by the system highlighted hallucination guardrails as the top priority fix. This turned a general worry about reliability into a specific task for the engineering team. Users appreciate how the tool translates complex evaluation data into simple, code-level recommendations.

Additional Information

Agent-EvalKit is developed by AWS Labs. It is built to integrate smoothly with Amazon Bedrock and other popular AI frameworks. The toolkit organizes the evaluation lifecycle into six distinct phases: Plan, Data, Trace, Run Agent, Eval, and Report. Each phase produces specific artifacts that feed into the next. The project is hosted on GitHub, and the full documentation and examples are available there for anyone who wants to learn more. The tool supports Python 3.11 and later versions and requires the uv package manager for installation.