AgentEval

AgentEval is the comprehensive .NET evaluation toolkit for AI agents, designed to be what RAGAS and DeepEval are for Python, but built natively for the Microsoft ecosystem. Specifically developed for Microsoft Agent Framework (MAF) and Microsoft.Extensions.AI, AgentEval provides sophisticated evaluation capabilities including tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison with enterprise-grade fluent assertion syntax.

The framework's standout feature is its ability to assert on tool chains like requirements using intuitive Should() syntax, allowing developers to verify that agents call tools in the correct sequence with proper arguments and timing. This capability is crucial for complex agent workflows where the order and accuracy of tool execution determines success or failure.

AgentEval's stochastic evaluation capability addresses the non-deterministic nature of LLM responses by running the same evaluation multiple times and asserting on success rates rather than single runs, providing statistically meaningful results. This approach recognizes that LLMs can produce different outputs for identical inputs, making single-run evaluations unreliable for production assessment.

The platform includes innovative trace record/replay functionality that captures live API interactions once and replays them infinitely without API costs, enabling consistent CI/CD evaluation workflows. This feature eliminates the variability and expense of live API calls during testing while maintaining realistic evaluation scenarios.

AgentEval's Red Team Security module evaluates against 192 attack probes covering 6 OWASP LLM Top 10 vulnerabilities with MITRE ATLAS technique mapping, testing for prompt injection, jailbreaks, PII leakage, and other security vulnerabilities. This comprehensive security evaluation is essential for enterprise AI applications where security breaches can have severe consequences.

Model comparison features provide side-by-side evaluation of different models with cost-quality recommendations, helping teams make data-driven decisions about which models to deploy. The platform can compare performance across GPT-4o, Claude, and other major providers while analyzing the cost-benefit tradeoffs.

With MIT licensing and a commitment to remaining open source forever, AgentEval offers full type safety, compile-time error checking, and deep IDE integration that leverages the strengths of the .NET ecosystem for enterprise AI agent development. This makes it particularly valuable for organizations already invested in Microsoft's technology stack.

Compared to Python alternatives like DeepEval or LangSmith, AgentEval provides native .NET integration with superior type safety and enterprise development practices, making it ideal for organizations prioritizing reliability and maintainability in their AI evaluation infrastructure.