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AgentEvalHQ
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AgentEvalHQ/AgentEval

AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison—built first for Microsoft Agent Framework (MAF) and Microsoft.Extensions.AI. What RAGAS, PromptFoo and DeepEval do for Python, AgentEval does for .NET

C# ◇ agent MIT
★154STARS
⑂16FORKS
!5ISSUES
🏆#9,604GLOBAL RANK
🔥2DAYS TRENDING
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+10

STARS · LAST 30 DAYS

1

PER DAY

#575

MOST-STARRED C#

Window7 days30 days90 days
Stars gained+7+10+90
Per day111
Forks gained+1+3+10

AgentEval gained 10 stars in the last 30 days, about 1 a day, and now has 154. It is about 1 year old and has averaged roughly 154 stars a year. It ranks #575 among C# repositories and #9,604 across all languages on GitHubRepo.

Trending Record

AgentEval has maintained a continuous presence across global trending indexes, peaking at #8704. Below is the 30-day activity profile:

💡 Overview

AgentEval is an open-source project written in C#: AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison—built first for Microsoft Agent Framework (MAF) and Microsoft.Extensions.AI. What RAGAS, PromptFoo and DeepEval do for Python, AgentEval does for .NET.

Engineered for speed, consistency, and developer ease, it solves common hurdles in agent, agentic, evals. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

⚡ Key Features

1

Optimized execution pipeline written in C# for predictable speed.

2

Zero-friction configuration with comprehensive sensible defaults out of the box.

3

Cross-platform runtime support across Linux, macOS, and Windows environments.

4

Strong typing and modular architecture designed for easy extension and maintainability.

5

Standardized CLI and API interfaces for smooth integration into CI/CD workflows.

6

Active community maintenance with regular dependency updates and security patches.

📥 Installation

terminal
$ git clone https://github.com/AgentEvalHQ/AgentEval.git
cd AgentEval

⚙ System Requirements

Platforms

  • • macOS
  • • Linux
  • • Windows

Runtime & Dependencies

C# environment and standard tooling

Architecture

x86_64, ARM64 (Apple Silicon & Graviton)

🧠 How It Works

AgentEval coordinates its core functionality through a modular C# pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

🎯 Production Use Cases

Autonomous AI Agents

Orchestrate intelligent workflows and tool-calling routines with AgentEval.

Model Inference & Prompting

Integrate fast, local or cloud-hosted generative AI models directly into production code.

Context Memory & RAG

Augment language models with dynamic vector retrieval and structured project memory.

Developer Productivity

Automate repetitive engineering tasks, code generation, and test creation using AI agents.

🚀 Getting Started

1

Install AgentEval using your package manager: `git clone https://github.com/AgentEvalHQ/AgentEval.git`

2

Initialize your project workspace or configuration file for AgentEval.

3

Import AgentEval into your codebase or invoke it directly from your terminal.

4

Execute your test suite or run `AgentEval --help` to verify successful setup.

👍 Strengths

Active community backing with 154 GitHub stars and verified adoption.
Permissive open-source distribution under the MIT license.
Built in C# for high execution speed and developer familiarity.
Cross-platform compatibility across modern Linux, macOS, and Windows environments.
Clean modular design allowing flexible configuration and pipeline integration.

⚠️ Considerations

Requires familiarity with C# and modern CLI workflows.
Ecosystem extensions may require manual configuration depending on environment constraints.
Active development roadmap means breaking API changes may occur across major versions.

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👥 Who Should Use This

Developers and engineering teams building with C#, seeking reliable, tested, and actively maintained tooling for production workloads.

🏆 Nearby in the Rankings

AgentEvalHQ/AgentEval is currently ranked #9,604 by stars across every repository tracked on GitHubRepo. These are adjacent projects:

RankRepositoryLanguageStarsAction
#9,576 jdp/twitterlibphp PHP ★ 155 Compare ↗
#9,576 mernen/completion-ruby Shell ★ 155 Compare ↗
#9,576 topfunky/sparklines Ruby ★ 155 Compare ↗
#9,576 callebtc/sayit Swift ★ 155 Compare ↗
#9,576 Asterisk4Magisk/AsteriskBOX Kotlin ★ 155 Compare ↗
#9,604 AgentEvalHQ/AgentEval This Project C# ★ 154
#9,604 andriydruk/LMPlayground Kotlin ★ 154 Compare ↗
#9,604 BabylonJS/Babylon-Lite TypeScript ★ 154 Compare ↗
#9,604 mmower/spike C ★ 154 Compare ↗
#9,604 notanumber/xapian-haystack Python ★ 154 Compare ↗
#9,604 notahat/simplesynth Objective-C ★ 154 Compare ↗

Frequently Asked Questions

What does AgentEval do? +

AgentEval is the comprehensive .NET toolkit for AI agent evaluation—tool usage validation, RAG quality metrics, stochastic evaluation, and model comparison—built first for Microsoft Agent Framework (MAF) and Microsoft.Extensions.AI. What RAGAS, PromptFoo and DeepEval do for Python, AgentEval does for .NET

What language is AgentEval written in? +

The primary language is C#. Topics include: agent, agentic, evals, evaluations, framework.

Is AgentEval actively maintained? +

Yes, the last recorded push was on Oct 2, 2026 with 5 open issues being tracked.

How many stars does AgentEval have? +

AgentEval has 154 stars and 16 forks on GitHub.

How does AgentEval rank among GitHub repositories? +

With 154 stars, AgentEvalHQ/AgentEval is ranked #9,604 globally across all repositories tracked on GitHubRepo and #575 among C# projects.

What license is AgentEval distributed under? +

The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.

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