The LLM Evaluation Framework
C-Rank Grade: B (Solid) - 47/100
External PR Merge Rate: 40%
Response Time: 3d
First Timer Success: 23%
confident-ai/deepeval has a recorded first-timer success rate of 23.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in confident-ai/deepeval respond to incoming external pull requests in approximately 62.7 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
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 46.8 places confident-ai/deepeval in the B tier.
The external contributor pull request merge rate for confident-ai/deepeval is 39.6%, based on public PR activity from non-core contributors.
confident-ai/deepeval currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Rigorous architecture standards (66% critical review signals). Ensure PRs strictly follow guidelines before requesting review in confident-ai/deepeval.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/confident-ai/deepeval?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 13:00 UTC (13:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 11:00 to 15:00 UTC (11:00 - 15:00 UTC local).
Limited recent maintainer review activity recorded for this repository. Prediction based on heuristic baseline.
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~41 hours (49% approval). Large PRs (>500 lines) take ~301 hours (18% approval). Keep your diff compact for rapid review.
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Hi folks - we recently released our plans for Typescript support, and we could borrow some help if some is available :) The current /typescript folder already contains and acts as a client wrapper to Confident AI, and currently already supports tracing. Our goal is to make local evals, simulation, and synthetic data generation, available in Typescript by July 1st. We're leaving out benchmarks on purpose because we don't believe that's what Typescript users want. Here are the initial TODOs: Support local evaluation models Support for complete metrics parity, with shared templates Support for unit test based testing workflows (similar to evals iterator, evaluate function, and deepeval test run) Post July 1st: Support for complete feature parity with regards to synthetic data generation Support for complete feature parity with regards to simulation Support for prompt optimization If you're interested, please comment below.
It could be useful to give users flexibility over the user prompt used to generate the goldens in the synthesizer. generate_goldens_from_contexts and a_generate_goldens_from_contexts may be overridden when inheriting from the Synthesizer class and rely on predefined prompt templates composed from specific arguments context, max_goldens_per_context, scenario, task, input_format deepeval/deepeval/synthesizer/synthesizer.py Lines 661 to 667 in a740ca3 prompt = SynthesizerTemplate.generate_synthetic_inputs( context=context, max_goldens_per_context=max_goldens_per_context, scenario=self.styling_config.scenario, task=self.styling_config.task, input_format=self.styling_config
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When evaluating whether to contribute to confident-ai/deepeval, 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 confident-ai/deepeval acknowledge new external contributions in approximately 2 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 39.6% were successfully merged into the primary branch.
confident-ai/deepeval has a recorded first-timer success rate of 23.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~2 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
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 46.8 places confident-ai/deepeval in the Solid tier.
The external pull request merge rate is 39.6%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, confident-ai/deepeval currently has 2 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for confident-ai/deepeval 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.