Open-source .NET toolkit for testing AI agents with fluent assertions, stochastic evaluation, red team security probes, and model comparison built for Microsoft Agent Framework.
A framework for testing whether AI agents actually accomplish their goals — measure performance before deploying to production.
AgentEval solves a problem most teams ignore until production breaks: how do you test AI agents that give different answers every time you run them?
Traditional software testing checks that output A equals expected B. AI agents don't work that way. Ask the same question twice, get two different answers. AgentEval handles this with stochastic evaluation. Run a test 50 times, assert that it passes 90% of attempts. That's closer to how agents actually behave in production.
This is a .NET toolkit. Full stop. If your team writes C# and builds on Microsoft Agent Framework (MAF) or Microsoft.Extensions.AI, AgentEval slots in naturally. If you work in Python, look at DeepEval, LangSmith, or RAGAS instead.
The .NET focus isn't a limitation for Microsoft shops. It's the only evaluation toolkit that speaks their language. Python has dozens of options. .NET had almost none until AgentEval showed up.
.Should() syntax. response.Should().MentionProduct("Widget") reads like English. Your QA team can understand these tests without learning evaluation theory.
Security: 192 attack probes covering 60% of the OWASP LLM Top 10. Prompt injection, jailbreaking, data extraction attempts. Run these before every deployment. The probes map to MITRE ATLAS techniques, so security teams get reports they understand.
RAG Quality: Faithfulness, relevance, context precision, and recall metrics for retrieval-augmented generation. Measures whether your agent actually uses the retrieved context or hallucinates.
Cost: Model comparison runs the same test across GPT-4, Claude, Gemini, and local models, then recommends the cheapest option that meets your quality bar.
Record an agent interaction once, replay it without hitting the LLM API. This saves money in CI/CD pipelines. Run 1,000 tests against recorded traces for $0 in API costs. Only hit the live API for new scenarios.
Source: agenteval.dev
AgentEval itself is free, but stochastic evaluation multiplies your LLM costs. Running each test 50 times means 50x the API calls. Use trace record/replay for regression testing and save live evaluations for new scenarios. Without this discipline, testing costs can exceed your production API spend.
IChatClient.AsEvaluableAgent() interface. Any .NET agent that implements IChatClient can be tested.
How does it compare to DeepEval?
DeepEval covers similar ground in Python with more metrics and a larger community. AgentEval is the .NET equivalent with stronger Microsoft integration and red team security features.
.NET developers building AI agents call AgentEval "the missing piece" for their testing pipeline. The fluent assertion syntax gets specific praise for readability. The trace record/replay feature is popular for keeping CI/CD costs down. Complaints focus on the small community (it's new), the .NET-only limitation, and the lack of a commercial support tier.
Without AgentEval, .NET teams either skip agent testing (risky) or build custom evaluation code (expensive). A senior .NET developer spending 2 weeks building evaluation infrastructure costs $5,000-10,000 in salary. AgentEval provides that infrastructure for $0. The 27 sample projects mean you're testing in hours, not weeks. For Python shops, DeepEval offers the same value proposition in their ecosystem.
Was this helpful?
AgentEval fills a critical gap: production-grade AI agent testing for the .NET ecosystem. The stochastic evaluation, red team probes, and trace replay are genuinely useful. Limited to .NET, which narrows the audience but deepens the value for Microsoft-stack teams.
AI-powered test case generation that creates comprehensive test suites based on agent capabilities and use cases.
Use Case:
Testing complex agents with many tools and capabilities without manually writing hundreds of test cases.
Built-in support for standard agent benchmarks like SWE-bench, HumanEval, and custom domain-specific evaluations.
Use Case:
Comparing agent performance against industry standards and tracking improvements over time.
Specialized testing for multi-agent systems including coordination evaluation, conversation quality, and collaboration effectiveness.
Use Case:
Ensuring multi-agent teams work together effectively and produce coherent, high-quality outputs.
Adversarial testing, jailbreaking attempts, and edge case evaluation to identify potential safety issues and failure modes.
Use Case:
Production safety validation for agents that handle sensitive data or high-stakes decisions.
Automated detection of performance degradation across agent versions with statistical significance testing.
Use Case:
Continuous integration pipelines that need to catch performance regressions before deployment.
Detailed analytics with trend analysis, performance comparisons, and exportable reports for stakeholder communication.
Use Case:
Demonstrating agent quality improvements to stakeholders and tracking development progress.
Free
month
Check website for pricing
Ready to get started with Agent Eval?
View Pricing Options →Production agent quality assurance
Continuous integration testing
Agent performance benchmarking
Safety and robustness validation
We believe in transparent reviews. Here's what Agent Eval doesn't handle well:
Agent Eval works with any agent that can be called via API or Python interface, including LangChain, CrewAI, AutoGen, and custom implementations.
Yes, the platform supports custom metrics, benchmarks, and evaluation criteria tailored to your specific use case.
Statistical testing methods, multiple evaluation runs, and fuzzy matching to handle the inherent variability in AI agent outputs.
Yes, with specialized tools for evaluating agent coordination, conversation quality, and collaborative task completion.
Weekly insights on the latest AI tools, features, and trends delivered to your inbox.
Red Team Security module launched with 192 OWASP LLM 2025 probes mapped to MITRE ATLAS techniques. Enhanced model comparison with automated cost/quality recommendations. Improved trace record/replay for CI/CD integration. Responsible AI metrics for toxicity, bias, and misinformation detection.
People who use this tool also find these helpful
Open-source LLM development platform for prompt engineering, evaluation, and deployment. Teams compare prompts side-by-side, run automated evaluations, and deploy with A/B testing. Free self-hosted or $20/month for cloud.
Visual AI testing platform that catches layout bugs, visual regressions, and UI inconsistencies your functional tests miss by understanding what users actually see.
Open-source LLM evaluation framework with 50+ research-backed metrics including hallucination detection, tool use correctness, and conversational quality. Pytest-style testing for AI agents with CI/CD integration.
Open-source LLM evaluation and testing platform by Comet for tracing, scoring, and benchmarking AI applications.
AI evaluation and guardrails platform for testing, validating, and securing LLM outputs in production applications.
Open-source LLM testing and evaluation framework for systematically testing prompts, models, and AI agent behaviors with automated red-teaming.
See how Agent Eval compares to Humanloop and other alternatives
View Full Comparison →Analytics & Monitoring
LLMOps platform for prompt engineering, evaluation, and optimization with collaborative workflows for AI product development teams.
Analytics & Monitoring
Tracing, evaluation, and observability for LLM apps and agents.
Testing & Quality
Open-source LLM testing and evaluation framework for systematically testing prompts, models, and AI agent behaviors with automated red-teaming.
No reviews yet. Be the first to share your experience!
Get started with Agent Eval and see if it's the right fit for your needs.
Get Started →Take our 60-second quiz to get personalized tool recommendations
Find Your Perfect AI Stack →Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.
Browse Agent Templates →