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📚Complete Guide

AgentEval Tutorial: Get Started in 5 Minutes [2026]

Master AgentEval with our step-by-step tutorial, detailed feature walkthrough, and expert tips.

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🔍 AgentEval Features Deep Dive

Explore the key features that make AgentEval powerful for voice agents workflows.

Fluent Tool-Chain Assertions

What it does:

Use case:

Stochastic Evaluation Runner

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Trace Record/Replay

What it does:

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Red Team Security Module

What it does:

Use case:

Model Comparison with Cost/Quality Recommendations

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❓ Frequently Asked Questions

Can I use AgentEval with Python agents?

No. AgentEval is built exclusively for .NET and ships on NuGet (nuget.org/packages/AgentEval). Python teams should use DeepEval, PromptFoo, or LangSmith for equivalent AI agent evaluation capabilities. Based on our analysis of 870+ AI tools, AgentEval is one of the only mature agent evaluation frameworks targeting the Microsoft/.NET ecosystem specifically, which is precisely its positioning.

Does AgentEval work with agents not built on Microsoft Agent Framework?

Yes. Any .NET agent that implements IChatClient can be tested via the IChatClient.AsEvaluableAgent() one-liner extension method. A Semantic Kernel bridge is also included for SK-based agents. This cross-framework design means you are not locked into MAF, though MAF is where the deepest integration exists with automatic tool call tracking and token/cost telemetry.

How does AgentEval compare to DeepEval and RAGAS?

DeepEval and RAGAS are Python frameworks with larger communities and broader metric catalogs. AgentEval is their .NET counterpart, offering equivalent coverage for RAG metrics (Faithfulness, Relevance, Context Precision/Recall), plus unique additions like the 192-probe Red Team module and fluent tool-chain assertions. Choose based on language ecosystem — AgentEval for C#/.NET shops, DeepEval/RAGAS for Python. All three are open source.

How much does stochastic testing cost in LLM API fees?

It scales with repetition count: 100 tests × 50 repetitions equals 5,000 LLM calls, roughly $15–$30 per test suite at GPT-4 pricing. AgentEval's recommended pattern is to use live stochastic evaluation only for new scenarios and switch to trace record/replay for regression testing in CI, which eliminates API costs entirely. The comparer's RunsPerModel option (typically 5) gives statistical stability without runaway cost.

What security vulnerabilities does the Red Team module detect?

The Red Team module runs 192 attack probes across 9 attack types: Prompt Injection, Jailbreaks, PII Leakage, System Prompt Extraction, Indirect Injection, Excessive Agency, Insecure Output Handling, API Abuse, and Encoding Evasion. This covers 6 of the OWASP LLM Top 10 2025 vulnerabilities (60% coverage) with MITRE ATLAS technique mapping, and results can be exported directly to PDF for compliance reporting via result.ExportAsync("security-report.pdf", ExportFormat.Pdf).

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Tutorial updated March 2026