EvalMy.AI

What it does

EvalMy.AI, a simple, easy-to-integrate service that automates the testing of LLM-based apps using C3-score metric (correctness, completeness, contradiction). It helps you quickly spot where your AI might fall short, reducing friction and speeding up testing.

EvalMy.AI is the == operator you need for automated testing of LLM applications

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  Evidently AI2024 · evidentlyai.com · ▲382

  Open-source evaluations and observability for LLM apps

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  Confident AI2024 · confident-ai.com · ▲86

  all-in-one LLM evaluation platform

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  Prompt-Engineering Tool: AI-to-AI Testing for LLM2023 · github.com · ▲37

  Spelltest framework simulates conversations between AI ‘synthetic users' in an environment to test and refine LLM-based applications. It ensures your app converse with utmost accuracy and relevance. Post-chat, Spelltest assesses responses, providing qualitative and quantitative feedback on performance. Suitable for both chat and completion modes. When to use: - After modifying your prompt. - When your LLM provider updates. - As a CI step for you repo. All feedback and collaborations appreciated!

• AE
  AI-Evals.io – Evaluate this site with the tools it reviewsFeb 2026 · ai-evals.io · ▲5

  I've been working on a site [1] to give people control of their LLM workflows through AI evals - automated checks that, once defined, let you move fast without regressions and cut through hype with proof. That one-liner is aimed at software engineers, but I've spent my career helping cross-functional teams collaborate, and that's really what this is about. AI agents make powerful workflows very plausible, but only if teams can grow them incrementally without losing control - no vendor lock-in, no discipline silos, no blind trust in outputs. The site tries to meet different audiences where…

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  Create LLM graders and run evals in JavaScript with one file2025 · github.com · ▲28

  Hi HN! Run it: OPENROUTER_API_KEY="sk" npx bff-eval --demo We built a tool to help people take LLM outputs and easily grade them / eval them to know how good an assistant response is. We've built a number of LLM apps, and while we could ship decent tech demos, we were disappointed with how they'd perform over time. We worked with a few companies who had the same problem, and found out scientifically building prompts and evals is far from a solved problem... writing these things feels more like directing a play than coding. Inspired by Anthropic's constitutional ai concepts, and amazing…

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  I implemented evals metrics for LLMs that runs locally on your machine2023 · github.com · ▲22

  The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.

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  Lovable▲1,477

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