Loading tool details…
Complete pricing guide for AgentEval. Compare all plans, analyze costs, and find the perfect tier for your needs.
Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether AgentEval is worth it →
Pricing sourced from AgentEval · Confirm current pricing and terms with the vendor.
A check mark means listed in that plan’s record. A dash means not listed, rather than confirmed unavailable. Verify current vendor terms.
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.
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.
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.
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.
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).
AI builders and operators use AgentEval to streamline their workflow.
Try AgentEval Now →DeepEval is the open-source, Pytest-style LLM evaluation framework from Confident AI. Ship it in your test suite the same way you ship pytest — it runs 50+ research-backed metrics (hallucination, tool-use correctness, contextual precision/recall, conversational quality) offline in dev and inline in CI/CD, then optionally streams the results to Confident AI's paid cloud for team dashboards and prompt versioning.
Compare Pricing →LangSmith helps developers trace, evaluate, test, and monitor applications built with language models and agent workflows.
Compare Pricing →Open-source CLI and library for testing, evaluating, and red-teaming LLM prompts, models, and RAG pipelines — runs locally on your machine or in CI.
Compare Pricing →