# AgentEvalNet
**Repository Path**: agent-projects-cn/agent-eval-net
## Basic Information
- **Project Name**: AgentEvalNet
- **Description**: AgentEval 是一款全面的 .NET 工具包，用于 AI 智能体评估——涵盖工具使用验证、检索增强生成（RAG）质量指标、随机评估和模型对比等功能——最初为微软智能体框架（MAF）和 Microsoft.Extensions.AI 打造。它之于 .NET，正如 RAGAS、PromptFoo 和 DeepEval 之于 Python。
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-05-25
- **Last Updated**: 2026-05-25
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# AgentEval
The .NET Evaluation Toolkit for AI Agents
---
AgentEval is **the comprehensive .NET toolkit for AI agent evaluation**—tool usage validation, RAG quality metrics, stochastic evaluation, model comparison, and memory benchmarks—built for **Microsoft Agent Framework (MAF)** and **Microsoft.Extensions.AI**. What RAGAS and DeepEval do for Python, AgentEval does for .NET, with the fluent assertion APIs .NET developers expect.
> **For years, agentic developers have imagined writing evaluations like this. Today, they can.**
> [!WARNING]
> **Preview — Use at Your Own Risk**
>
> This project is **experimental (work in progress)**. APIs and behavior may change without notice.
> **Do not use in production or safety-critical systems** without independent review, testing, and hardening.
>
> Portions of the code, tests, and documentation were created with assistance from AI tools and reviewed by maintainers.
> Despite review, errors may exist — you are responsible for validating correctness, security, and compliance for your use case.
>
> Licensed under the **MIT License** — provided **"AS IS"** without warranty. See [LICENSE](LICENSE) and [DISCLAIMER.md](DISCLAIMER.md).
---
## The Code You Have Been Dreaming Of
### 🥇 Assert on Tool Chains Like You Have Always Imagined
The .NET fluent API for agentic tool usage. Every assertion you wished existed — order, arguments, duration, errors — composable, with `because:` reasoning baked in.
```csharp
result.ToolUsage!.Should()
.HaveCalledTool("SearchFlights", because: "must search before booking")
.WithArgument("destination", "Paris")
.WithDurationUnder(TimeSpan.FromSeconds(2))
.And()
.HaveCalledTool("BookFlight", because: "booking follows search")
.AfterTool("SearchFlights")
.WithArgument("flightId", "AF1234")
.And()
.HaveCallOrder("SearchFlights", "BookFlight", "SendConfirmation")
.HaveNoErrors();
```
**No more regex parsing logs. No more "did it call that function?"** — just IntelliSense-driven assertions that read like requirements.
---
### 🥈 Stochastic Evaluation: Because LLMs Are Non-Deterministic
A single evaluation run might pass 70% of the time due to LLM randomness. Stochastic evaluation tells you the **actual** reliability — pass/fail on the *rate*, not the lucky run.
```csharp
var result = await stochasticRunner.RunStochasticTestAsync(
agent, testCase,
new StochasticOptions
{
Runs = 20, // Run 20 times
SuccessRateThreshold = 0.85, // 85% must pass
ScoreThreshold = 75 // Min score to count as "pass"
});
result.Statistics.Mean.Should().BeGreaterThan(80); // avg quality
result.Statistics.StandardDeviation.Should().BeLessThan(10); // consistency
Assert.True(result.PassedThreshold,
$"Success rate {result.SuccessRate:P0} below 85% threshold");
```
**The evaluation that never flakes.** Mean + StdDev + SuccessRate, not pass/fail roulette.
---
### 🥉 Workflow Evaluation: Multi-Agent Flows as Executable Assertions
MAF workflows are powerful — and finally testable. Assert on executor order, edges traversed, tools called across the graph, and end-to-end SLAs.
```csharp
var testCase = new WorkflowTestCase
{
Name = "TripPlanner — Tokyo & Beijing",
Input = "Plan a 7-day trip to Tokyo and Beijing — flights and hotels",
ExpectedExecutors = ["TripPlanner", "FlightReservation", "HotelReservation", "Presenter"],
StrictExecutorOrder = true,
ExpectedTools = ["SearchFlights", "BookFlight", "BookHotel"],
MaxDuration = TimeSpan.FromMinutes(2),
};
var harness = new WorkflowEvaluationHarness();
var result = await harness.RunWorkflowTestAsync(workflowAdapter, testCase);
result.ExecutionResult!.Should()
.HaveSucceeded(because: "the trip must be planned end-to-end")
.HaveExecutedInOrder("TripPlanner", "FlightReservation", "HotelReservation", "Presenter")
.HaveAnyExecutorCalledTool("SearchFlights")
.HaveAnyExecutorCalledTool("BookHotel")
.HaveTraversedEdge("TripPlanner", "FlightReservation")
.HaveCompletedWithin(TimeSpan.FromMinutes(2))
.HaveNoToolErrors();
```
**4 agents, 5 tools, one test.** Execution timeline, edge traversal, tool errors — all observable, all assertable.
---
### Performance SLAs as Executable Evaluations
```csharp
result.Performance!.Should()
.HaveTotalDurationUnder(TimeSpan.FromSeconds(5),
because: "UX requires sub-5s responses")
.HaveTimeToFirstTokenUnder(TimeSpan.FromMilliseconds(500),
because: "streaming responsiveness matters")
.HaveEstimatedCostUnder(0.05m,
because: "stay within $0.05/request budget")
.HaveTokenCountUnder(2000);
```
**Know before production if your agent is too slow or too expensive.**
---
### Behavioral Policy Guardrails (Compliance as Code)
```csharp
result.ToolUsage!.Should()
// PCI-DSS: Never expose card numbers
.NeverPassArgumentMatching(@"\b\d{16}\b",
because: "PCI-DSS prohibits raw card numbers")
// GDPR: Require consent
.MustConfirmBefore("ProcessPersonalData",
because: "GDPR requires explicit consent",
confirmationToolName: "VerifyUserConsent")
// Safety: Block dangerous operations
.NeverCallTool("DeleteAllCustomers",
because: "mass deletion requires manual approval");
```
---
### Compare Models, Get a Winner, Ship with Confidence
```csharp
var stochasticRunner = new StochasticRunner(harness);
var comparer = new ModelComparer(stochasticRunner);
var result = await comparer.CompareModelsAsync(
factories: new IAgentFactory[]
{
new AzureModelFactory("gpt-4o", "GPT-4o"),
new AzureModelFactory("gpt-4o-mini", "GPT-4o Mini"),
new AzureModelFactory("gpt-35-turbo", "GPT-3.5 Turbo")
},
testCases: agenticTestSuite,
metrics: new[] { new ToolSuccessMetric(), new RelevanceMetric(evaluator) },
options: new ComparisonOptions(RunsPerModel: 5));
Console.WriteLine(result.ToMarkdown());
```
**Output:**
```markdown
## Model Comparison Results
| Rank | Model | Tool Accuracy | Relevance | Mean Latency | Cost/1K Req |
|------|---------------|---------------|-----------|--------------|-------------|
| 1 | GPT-4o | 94.2% | 91.5 | 1,234ms | $0.0150 |
| 2 | GPT-4o Mini | 87.5% | 84.2 | 456ms | $0.0003 |
| 3 | GPT-3.5 Turbo | 72.1% | 68.9 | 312ms | $0.0005 |
**Recommendation:** GPT-4o - Highest tool accuracy (94.2%)
**Best Value:** GPT-4o Mini - 87.5% accuracy at 50x lower cost
```
---
### Combined: Stochastic + Model Comparison
The most powerful pattern — compare models with statistical rigor (see Sample D4):
```csharp
var factories = new IAgentFactory[]
{
new AzureModelFactory("gpt-4o", "GPT-4o"),
new AzureModelFactory("gpt-4o-mini", "GPT-4o Mini")
};
var modelResults = new List<(string ModelName, StochasticResult Result)>();
foreach (var factory in factories)
{
var result = await stochasticRunner.RunStochasticTestAsync(
factory, testCase,
new StochasticOptions(Runs: 5, SuccessRateThreshold: 0.8));
modelResults.Add((factory.ModelName, result));
}
modelResults.PrintComparisonTable();
```
**Output:**
```
+------------------------------------------------------------------------------+
| Model Comparison (5 runs each) |
+------------------------------------------------------------------------------+
| Model | Pass Rate | Mean Score | Std Dev | Recommendation |
+--------------+-------------+------------+----------+------------------------+
| GPT-4o | 100% | 92.4 | 3.2 | Best Quality |
| GPT-4o Mini | 80% | 84.1 | 8.7 | Best Value |
+------------------------------------------------------------------------------+
```
---
### RAG Quality: Is Your Agent Hallucinating?
```csharp
var context = new EvaluationContext
{
Input = "What are the return policy terms?",
Output = agentResponse,
Context = retrievedDocuments,
GroundTruth = "30-day return policy with receipt"
};
var faithfulness = await new FaithfulnessMetric(evaluator).EvaluateAsync(context);
var relevance = await new RelevanceMetric(evaluator).EvaluateAsync(context);
var correctness = await new AnswerCorrectnessMetric(evaluator).EvaluateAsync(context);
// Detect hallucinations
if (faithfulness.Score < 70)
throw new HallucinationDetectedException($"Faithfulness: {faithfulness.Score}");
```
---
### Red Team Security Evaluation: Find Vulnerabilities Before Production
AgentEval includes comprehensive red team security evaluation with **192 probes across 9 attack types**, covering **6/10 OWASP LLM Top 10 2025** categories and **6 MITRE ATLAS** techniques:
```csharp
// Sample20: Basic RedTeam evaluation
var redTeam = new RedTeamRunner();
var result = await redTeam.RunAsync(agent, new RedTeamOptions
{
AttackTypes = new[] {
AttackType.PromptInjection,
AttackType.Jailbreak,
AttackType.PIILeakage,
AttackType.ExcessiveAgency, // LLM06
AttackType.InsecureOutput // LLM05
},
Intensity = AttackIntensity.Quick,
ShowFailureDetails = true // Show actual attack probes (for analysis)
});
// Comprehensive security validation
result.Should()
.HaveOverallScoreAbove(85, because: "security threshold for production")
.HaveAttackSuccessRateBelow(0.15, because: "max 15% attack success allowed")
.ResistAttack(AttackType.PromptInjection, because: "must block injection attempts");
```
**Real-time security assessment:**
```
╔══════════════════════════════════════════════════════════════════════════════╗
║ RedTeam Security Assessment ║
╠══════════════════════════════════════════════════════════════════════════════╣
║ 🛡️ Overall Score: 88.2% ║
║ Verdict: ✅ PARTIAL_PASS ║
║ Duration: 12.4s | Agent: ResearchAssistant ║
║ Probes: 45 total, 40 resisted, 5 compromised ║
╠══════════════════════════════════════════════════════════════════════════════╣
║ Attack Results: ║
║ ║
║ Attack Resisted Rate Severity ║
║ ─────────────────────────────────────────────────────────────────────── ║
║ ✅ Prompt Injection 8/9 89% Critical ║
║ ✅ Jailbreak 7/8 88% High ║
║ ✅ PII Leakage 6/6 100% Critical ║
║ ✅ Excessive Agency 5/5 100% High ║
║ ❌ Insecure Output 10/12 83% Critical ║
║ OWASP: LLM05 | MITRE: AML.T0051 ║
╚══════════════════════════════════════════════════════════════════════════════╝
```
**Multiple export formats** for security teams:
- **JSON** for automation and tooling
- **Markdown** for human-readable reports
- **JUnit XML** for CI/CD integration
- **SARIF** for GitHub Security tab integration
- **PDF** for executive/board-level reporting
**✅ See Samples:** [Sample20_RedTeamBasic.cs](samples/AgentEval.Samples/Sample20_RedTeamBasic.cs) • [Sample21_RedTeamAdvanced.cs](samples/AgentEval.Samples/Sample21_RedTeamAdvanced.cs) • [docs/redteam.md](docs/redteam.md)
---
### Responsible AI: Content Safety Metrics
Complementing security evaluation, AgentEval's ResponsibleAI namespace provides **content safety evaluation**:
```csharp
using AgentEval.Metrics.ResponsibleAI;
// Toxicity detection (pattern + LLM hybrid)
var toxicity = new ToxicityMetric(chatClient, useLlmFallback: true);
var toxicityResult = await toxicity.EvaluateAsync(context);
// Bias measurement with counterfactual testing
var bias = new BiasMetric(chatClient);
var biasResult = await bias.EvaluateCounterfactualAsync(
originalContext, counterfactualContext, "gender");
// Misinformation risk assessment
var misinformation = new MisinformationMetric(chatClient);
var misInfoResult = await misinformation.EvaluateAsync(context);
// All must pass for responsible AI compliance
toxicityResult.Should().HaveScoreAbove(90);
biasResult.Should().HavePassed();
misInfoResult.Should().HavePassed();
```
| Metric | Type | Detects |
|--------|------|--------|
| **ToxicityMetric** | Hybrid | Hate speech, violence, harassment |
| **BiasMetric** | LLM | Stereotyping, differential treatment |
| **MisinformationMetric** | LLM | Unsupported claims, false confidence |
**✅ See:** [docs/ResponsibleAI.md](docs/ResponsibleAI.md)
---
### Memory Evaluation: Does Your Agent Actually Remember?
AgentEval ships **AgentEval.Memory** — the comprehensive .NET toolkit for evaluating agent memory: retention, recall depth across long contexts, temporal reasoning, fact-update handling, cross-session persistence, and resistance to distractor turns.
```csharp
// One-line benchmark with grade
var runner = MemoryBenchmarkRunner.Create(chatClient);
var agent = chatClient.AsEvaluableAgent(name: "MemoryAgent", includeHistory: true);
var result = await runner.RunBenchmarkAsync(agent, MemoryBenchmark.Standard);
Console.WriteLine($"Memory: {result.OverallScore:F1}% ({result.Grade})");
// Save baseline + generate an interactive HTML pentagon report
var store = new JsonFileBaselineStore();
await store.SaveAsync(result.ToBaseline(label: "GPT-4o"));
await result.ExportHtmlReportAsync("memory-report.html");
```
**What's in the box:**
| Capability | Detail |
|---|---|
| **5 memory metrics** | Retention, ReachBack, Temporal, NoiseResilience, ReducerFidelity |
| **5 benchmark presets** | Quick (3 cats) → Standard (8) → Full (12) → Diagnostic / Overflow (192K-token haystacks) |
| **HTML pentagon reports** | Multi-model overlay, baseline diffs, drill-down judge explanations |
| **LongMemEval (ICLR 2025)** | Fully re-implemented in .NET — paper-comparable scoring (GPT-4o = 57.7%) |
| **MAF-native** | Compatible with `AIContextProvider`, `ChatHistoryProvider`, `CompactionStrategy` |
| **Custom scenarios** | Build your own with `MemoryFact` / `MemoryQuery` / `MemoryTestRunner` |
**Honest caveats:**
- The native `Standard` benchmark currently scores ~88–93% on GPT-4.1 — strong models clear it comfortably. Use it as a **regression gate** for your own delta over time, and use **LongMemEval** (Sample G7) for cross-platform comparable numbers. Harder synthesis/counterfactual scenarios are on the way.
- Memory evaluation **always calls a real LLM** (the judge can't be mocked).
- LongMemEval dataset isn't redistributed — [download it from HuggingFace](https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned).
**✅ See:** [docs/memory-evaluation.md](docs/memory-evaluation.md) • [docs/maf-memory-integration.md](docs/maf-memory-integration.md) • [Sample G2: Memory Benchmark](samples/AgentEval.Samples/MemoryEvaluation/02_MemoryBenchmarkDemo.cs) • [Sample G7: LongMemEval](samples/AgentEval.Samples/MemoryEvaluation/07_LongMemEvalBenchmarkDemo.cs)
---
## Why AgentEval?
| Challenge | How AgentEval Solves It |
|-----------|------------------------|
| "What tools did my agent call?" | **Full tool timeline** with arguments, results, timing |
| "Evaluations fail randomly!" | **stochastic evaluation** - assert on pass *rate*, not pass/fail |
| "Which model should I use?" | **Model comparison** with cost/quality recommendations |
| "Is my agent compliant?" | **Behavioral policies** - guardrails as code |
| "Is my RAG hallucinating?" | **Faithfulness metrics** - grounding verification |
| "What's the latency/cost?" | **Performance metrics** - TTFT, tokens, estimated cost |
| "How do I debug failures?" | **Trace recording** - capture executions for step-by-step analysis |
| "Is my agent secure?" | **Red Team evaluation** - 192 probes, OWASP LLM 2025 coverage |
| "Is content safe and unbiased?" | **ResponsibleAI metrics** - toxicity, bias, misinformation |
| "Does my agent actually remember?" | **Memory evaluation** - retention, reach-back, temporal, LongMemEval (ICLR 2025) |
---
## Who Is AgentEval For?
**🏢 .NET Teams Building AI Agents** — If you're building production AI agents in .NET and need to verify tool usage, enforce SLAs, handle non-determinism, or compare models—AgentEval is for you.
**🚀 Microsoft Agent Framework (MAF) Developers** — Native integration with MAF concepts: `AIAgent`, `IChatClient`, automatic tool call tracking, and performance metrics with token usage and cost estimation.
**📊 ML Engineers Evaluating LLM Quality** — Rigorous evaluation capabilities: RAG metrics (Faithfulness, Relevance, Context Precision), embedding-based similarity, and calibrated judge patterns for consistent evaluation.
---
## The .NET Advantage
| Feature | AgentEval | Python Alternatives |
|---------|-----------|---------------------|
| **Language** | Native C#/.NET | Python only |
| **Type Safety** | Compile-time errors | Runtime exceptions |
| **IDE Support** | Full IntelliSense | Variable |
| **MAF Integration** | First-class | None |
| **Fluent Assertions** | `Should().HaveCalledTool()` | N/A |
| **Trace Replay** | Built-in | Manual setup |
---
## Key Features
### Core Features
- Fluent assertions - tool order, arguments, results, duration
- Stochastic evaluation - run N times, analyze statistics (mean, std dev, p90)
- Model comparison - compare across models with recommendations
- Trace recording - capture executions for debugging and reproduction
- Performance assertions - latency, TTFT, tokens, cost
### Evaluation Coverage
- Red Team security - 192 probes, OWASP LLM 2025, MITRE ATLAS coverage
- Responsible AI - toxicity, bias, misinformation detection
- **Memory evaluation** - retention, reach-back, temporal, cross-session, HTML pentagon reports, LongMemEval (ICLR 2025)
- Multi-turn conversations - full conversation flow evaluation
- Workflow evaluation - multi-agent orchestration and routing
- Snapshot evaluation - regression detection with semantic similarity
### Metrics
- RAG metrics - faithfulness, relevance, context precision/recall, correctness
- Agentic metrics - tool selection, arguments, success, efficiency
- Embedding metrics - semantic similarity (100x cheaper than LLM)
- Custom metrics - extensible for your domain
### Developer Experience
- Rich output - configurable verbosity (None/Summary/Detailed/Full)
- Time-travel traces - step-by-step execution capture in JSON
- Trace artifacts - auto-save traces for failed evaluations
- Behavioral policies - NeverCallTool, MustConfirmBefore, NeverPassArgumentMatching
### CLI Tool
- `agenteval init / doctor / migrate` - Bootstrap, validate, and migrate the `.agenteval/` workspace (canonical output store with audit-chain integrity)
- `agenteval bench {gdpr,eu-ai-act,agentic}` - Run compliance and agentic benchmark suites
- `agenteval compliance render` / `agenteval render --benchmark agentic` - Re-render reports from existing evidence (no LLM cost)
- `agenteval mc serve / mc doctor` - Launch and verify the Mission Control web portal (read-only viewer over `.agenteval/`)
- CI/CD-friendly exit codes; multiple export formats via `agenteval render`
### Mission Control Portal
- Single-binary web portal (Hot Chocolate 16 GraphQL + minimal REST + React SPA) served on `http://localhost:5000`
- Read-only view over `.agenteval/`: dashboard, runs list, recursive `EvalResult` tree drill-down, compliance matrix with audit-chain badges, evaluator registry, per-evaluator timeline
- Single-port deployment via `agenteval mc serve`, `dotnet run --project src/AgentEval.MissionControl`, or `docker compose up`
- See [`docs/missioncontrol/getting-started.md`](docs/missioncontrol/getting-started.md)
### Compliance Benchmarks
- **GDPR** — composite-eval audit suite covering 16 articles across 5 pillars; smoke / standard / audit presets + healthcare / HR / children's domain packs
- **EU AI Act** — Regulation (EU) 2024/1689 high-risk obligations; smoke / standard / audit presets + high-risk-employment / -credit / -education domain packs
- **Agentic** — Foundry-equivalent 60-evaluator suite (11 presets covering tool calls, RAG quality, judge quality, safety, telemetry, stochastic stability, conversational, reasoning)
- Every evidence document cryptographically chained to its source run; `agenteval doctor` re-validates on demand
### `.agenteval/` Workspace Standard
- Canonical on-disk format: one folder per agent / workflow, deterministic run IDs, SHA-256 content hashes on every manifest
- Read-only consumed by Mission Control; written by the CLI, test harnesses, and benchmark runners
- See [`docs/agenteval-workspace.md`](docs/agenteval-workspace.md)
### Cross-Framework & DI
- Universal `IChatClient.AsEvaluableAgent()` one-liner for any AI provider
- Dependency Injection via `services.AddAgentEval()` / `services.AddAgentEvalAll()`
- Semantic Kernel bridge via `AIFunctionFactory.Create()` (see NuGetConsumer sample)
### Integration
- CI/CD integration - JUnit XML, Markdown, JSON, SARIF export
- Benchmarks - custom patterns with dataset loaders (JSON, YAML, CSV, JSONL)
- Comprehensive multi-framework evaluation suite across all supported TFMs
---
## Installation
```bash
dotnet add package AgentEval --prerelease
```
**Compatibility:**
| Dependency | Version |
|------------|----------|
| Microsoft Agent Framework (MAF) | `1.3.0` |
| Microsoft.Extensions.AI | `10.5.0` |
| .NET | 8.0, 9.0, 10.0 |
**Single package, modular internals:**
- `AgentEval.Abstractions` — Public contracts and interfaces
- `AgentEval.Core` — Metrics, assertions, comparison, tracing
- `AgentEval.DataLoaders` — Data loading and export
- `AgentEval.MAF` — Microsoft Agent Framework integration
- `AgentEval.Memory` — Memory evaluation, benchmarks, LongMemEval, HTML reporting
- `AgentEval.RedTeam` — Security testing
**CLI Tool:**
The CLI ships in-tree but is not yet published as a `dotnet tool`. Run via:
```bash
dotnet run --project src/AgentEval.Cli -- init
dotnet run --project src/AgentEval.Cli -- bench gdpr --preset smoke --subject MyAgent
dotnet run --project src/AgentEval.Cli -- mc serve
```
Once published, the equivalent will be:
```bash
dotnet tool install -g AgentEval.Cli --prerelease # planned
agenteval init
agenteval bench gdpr --preset smoke --subject MyAgent
agenteval mc serve
```
> **v1 NuGet scope.** The `AgentEval` package currently ships `AgentEval.{Abstractions,Core,DataLoaders,MAF,RedTeam}`. The agentic 60-evaluator suite, GDPR/EU AI Act benchmark code, and memory evaluation pack live alongside the `agenteval` CLI but are not yet exposed as programmatic NuGet APIs — they are runnable today via the CLI binaries. Surfacing them as separate packages is on the v1.1 roadmap.
**Supported Frameworks:** .NET 8.0, 9.0, 10.0
---
## Quick Start
See the **[Getting Started Guide](docs/getting-started.md)** for a complete walkthrough with code examples.
---
## Documentation
| Guide | Description |
|-------|-------------|
| [Getting Started](docs/getting-started.md) | Your first agent evaluation in 5 minutes |
| [Fluent Assertions](docs/assertions.md) | Complete assertion guide |
| [stochastic evaluation](docs/stochastic-evaluation.md) | Handle LLM non-determinism |
| [Model Comparison](docs/model-comparison.md) | Compare models with confidence |
| [Benchmarks](docs/benchmarks.md) | Benchmark patterns and best practices |
| [Tracing](docs/tracing.md) | Record and Replay patterns |
| [Red Team Security](docs/redteam.md) | Security probes, OWASP/MITRE coverage |
| [Responsible AI](docs/ResponsibleAI.md) | Toxicity, bias, misinformation detection |
| [Memory Evaluation](docs/memory-evaluation.md) | Retention, reach-back, temporal, LongMemEval, HTML reports |
| [MAF Memory Integration](docs/maf-memory-integration.md) | How AgentEval.Memory maps to MAF 1.3.0 pipelines |
| [Cross-Framework](docs/cross-framework.md) | Semantic Kernel, IChatClient adapters |
| [CLI Tool](docs/cli.md) | Command-line evaluation guide |
| [Migration Guide](docs/comparison.md) | Coming from Python/Node.js frameworks |
| [Code Gallery](docs/showcase/code-gallery.md) | Stunning code examples |
---
## Samples
Run the included samples, organised into groups:
```bash
dotnet run --project samples/AgentEval.Samples
```
The interactive menu lets you select a **group** (A–G), then a **sample** within it.
| Group | Focus | Samples |
|-------|-------|---------|
| **A — Getting Started** ★ no credentials | Hello World, tool tracking, performance basics, MAF integration patterns | 7 |
| **B — Metrics & Quality** | RAG evaluation, quality metrics, judge calibration, responsible AI | 5 |
| **C — Workflows & Conversations** | Multi-turn conversations, MAF workflows, source-gen executors | 4 |
| **D — Performance & Statistics** | Latency profiling, stochastic evaluation, model comparison, streaming | 5 |
| **E — Safety & Security** | Policy guardrails, red team scanning, OWASP compliance | 3 |
| **F — Data & Infrastructure** | Snapshot testing, datasets, trace replay, benchmarks, cross-framework | 7 |
| **G — Memory Evaluation** | Memory basics, benchmarks, scenarios, DI, cross-session, HTML reports, LongMemEval (ICLR 2025) | 10 |
**41 samples in total.** See [samples/AgentEval.Samples/README.md](samples/AgentEval.Samples/README.md) for the full listing with per-sample descriptions, timing, and credential requirements.
---
## CI Status
| Workflow | Status |
|----------|--------|
| Build & Test | [](https://github.com/AgentEvalHQ/AgentEval/actions/workflows/ci.yml) |
| Security Scan | [](https://github.com/AgentEvalHQ/AgentEval/actions/workflows/security.yml) |
| Documentation | [](https://github.com/AgentEvalHQ/AgentEval/actions/workflows/docs.yml) |
---
## Contributing
We welcome contributions! Please see:
- [CONTRIBUTING.md](CONTRIBUTING.md)
- [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md)
- [SECURITY.md](SECURITY.md)
---
## Commercial & Enterprise
AgentEval is MIT and community-driven. For enterprise inquiries, see: https://agenteval.dev/commercial.html
---
## Forever Open Source
AgentEval is **MIT licensed** and will remain open source forever. We believe in:
- ✅ **No license changes** — MIT today, MIT forever
- ✅ **No bait-and-switch** — core stays MIT and fully usable
- ✅ **Community first** — built with the .NET AI community
- ℹ️ **Optional add-ons may exist separately** (if/when built)
---
## License
MIT License. See [LICENSE](LICENSE) for details.
---
Built with love for the .NET AI community
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---
## Star History
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