AgentEvalHQ/AgentEval - Open Source PR Review Scorecard

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-Rank Grade: B (Solid) - 67/100

External PR Merge Rate: 93%

Response Time: 16d

First Timer Success: 100%

Frequently Asked Questions

Is AgentEvalHQ/AgentEval welcoming to first-time open-source contributors?

AgentEvalHQ/AgentEval has a recorded first-timer success rate of 100.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in AgentEvalHQ/AgentEval respond to incoming external pull requests in approximately 374.8 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 66.9 C-Rank™ score (B Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 66.9 places AgentEvalHQ/AgentEval in the B tier.

What is the external contributor pull request merge rate for AgentEvalHQ/AgentEval?

The external contributor pull request merge rate for AgentEvalHQ/AgentEval is 92.6%, based on public PR activity from non-core contributors.

Are there Good First Issues available in AgentEvalHQ/AgentEval?

AgentEvalHQ/AgentEval does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

AgentEvalHQ
AgentEvalHQ/AgentEvalBSolid136
GitHub
Back to Explorer
AgentEvalHQ

AgentEvalHQ/AgentEval

136
BSolid(67/100)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

Compare
Jump to:
Response Velocity
15 days+
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
92.6%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
100.0%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
1 core
Single maintainer review bottleneck
Diagnostic Health HUD
92.6%
Merge Gauge
100.0%
1st-Timer
Community Vibe67/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for AgentEvalHQ/AgentEval
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/AgentEvalHQ/AgentEval)](https://getmerged.abhishekco.de/AgentEvalHQ/AgentEval?utm_source=github&utm_medium=badge)

Active Good First Issues (0)

View on GitHub

No cached good first issues currently tracked for AgentEvalHQ/AgentEval.

View all good first issues directly on GitHub

Looking for more C# beginner tasks?Explore C# GFI

Contributor Community Vibe Feedback

Rate what actually matters after opening a pull request here.

Have you contributed to this repo?

Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.

3 ratings required
Maintainer helpfulness
Review speed
Beginner friendliness

Contributor Compatibility & Review Speed Analysis for AgentEvalHQ/AgentEval

When evaluating whether to contribute to AgentEvalHQ/AgentEval, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of AgentEvalHQ/AgentEval acknowledge new external contributions in approximately 15 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 92.6% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to AgentEvalHQ/AgentEval

01

Is AgentEvalHQ/AgentEval welcoming to first-time open-source contributors?

AgentEvalHQ/AgentEval has a recorded first-timer success rate of 100.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~15 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 66.9 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 66.9 places AgentEvalHQ/AgentEval in the Solid tier.

04

What is the external contributor pull request merge rate for AgentEvalHQ/AgentEval?

The external pull request merge rate is 92.6%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in AgentEvalHQ/AgentEval?

AgentEvalHQ/AgentEval does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for AgentEvalHQ/AgentEval are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.