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AI · alternatives · 2026

24 alternatives to Giskard – Testing framework dedicated to LLMs and Tabular ML models

🐢 Open-Source Evaluation & Testing library for LLM Agents - Giskard-AI/giskard-oss

Giskard is a testing framework designed for large language models and tabular machine learning models. It helps developers systematically test and validate these models for reliability and performance issues. The… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Giskard – Testing framework dedicated to LLMs and Tabular ML models launched in 2023; newer entries below may have overtaken it.

  1. 1
    Giskard▲521

    Open-source testing framework for LLMs & ML models

    2023 · github.com · its alternatives →

  2. 2

    Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…

    2025 · github.com · its alternatives →

  3. 3

    🐢 Open-Source Evaluation & Testing library for LLM Agents - Giskard-AI/giskard-oss

    2024 · github.com · its alternatives →

  4. 4PL

    Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https:&#x2F;&#x2F;giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…

    2023 · docs.giskard.ai · its alternatives →

  5. 5

    The low-code platform for testing AI apps

    2024 · langtail.com · its alternatives →

  6. 6

    Hey, Jared Palmer (creator of this playground) here. Really excited to ship this. I’ve been building this over the past few weeks to compare LLMs from different providers like OpenAI, Anthropic, Cohere, etc. At Vercel, I manage our Frameworks division (including Next.js, Svelte, and Turbo) and wanted to also dogfood some of the latest features in a slightly larger application. This playground takes a lot of inspiration from https:&#x2F;&#x2F;nat.dev and is built on Tailwind, ui.shadcn.com, and some upcoming Vercel products we’re announcing soon. We’re going to continue adding models to…

    2023 · play.vercel.ai · its alternatives →

  7. 7

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

    2023 · github.com · its alternatives →

  8. 8
    LLM Stats▲293

    I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed&#x2F;throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https:&#x2F;&#x2F;llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…

    Oct 2025 · llm-stats.com · its alternatives →

  9. 9

    I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…

    2025 · nature.com · its alternatives →

  10. 10

    Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)

    2023 · chainforge.ai · its alternatives →

  11. 11

    I built this game as a playful way to train code reading skills. You're dropped into a random location in a real GitHub repo where some lines code are hidden. The goal is to pick up clues from the surrounding code and fill in the missing lines as quickly as possible. It currently has curated game content for Python, TypeScript, JavaScript (by most metrics the top 3 languages on GitHub). I had the idea when it dawned on me that reviewing and understanding code spat out by an LLM is a key skill for the AI era. (I sincerely hope you're critically reviewing what your favorite LLM is serving…

    Feb 2026 · gitguessr.com · its alternatives →

  12. 12

    AI Infra / AI Orchestration / AI Control Plane. Contribute to polyaxon/polyaxon development by creating an account on GitHub.

    2017 · github.com · its alternatives →

  13. 13

    Hey HN! I'm Caleb, one of the contributors to Opik, a new open source framework for LLM evaluations. Over the last few months, my colleagues and I have been working on a project to solve what we see as the most painful parts of writing evals for an LLM application. For this initial release, we've focused on a few core features that we think are the most essential: - Simplifying the implementation of more complex LLM-based evaluation metrics, like Hallucination and Moderation. - Enabling step-by-step tracking, such that you can test and debug each individual component of your LLM application,…

    2024 · github.com · its alternatives →

  14. 14

    open-source agentic AI data assistant for the next generation of AI + Data products. - eosphoros-ai/DB-GPT

    2023 · github.com · its alternatives →

  15. 15

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

    2023 · github.com · its alternatives →

  16. 16

    G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…

    2023 · github.com · its alternatives →

  17. 17

    A general-purpose encoder-decoder framework for Tensorflow - google/seq2seq

    2017 · github.com · its alternatives →

  18. 18

    Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation - mljar/mljar-supervised

    2021 · github.com · its alternatives →

  19. 19AT

    I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens&#x2F;sec): Overall output fluency Demo: https:&#x2F;&#x2F;llmapitest.com&#x2F; Code: https:&#x2F;&#x2F;github.com&#x2F;qjr87&#x2F;llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

    2025 · llmapitest.com · its alternatives →

  20. 20

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

    2023 · github.com · its alternatives →

  21. 21

    I am excited to announce the release of TabPFN-2.5, our tabular foundation model that now scales to datasets of up to 50,000 samples and 2,000 features - a 5x increase from TabPFN v2, published in the Nature journal earlier this year. TabPFN-2.5 delivers state-of-the-art predictions in one forward pass without hyperparameter tuning across classification and regression tasks. What’s new in 2.5: TabPFN-2.5 maintains the core approach of v2 - a pretrained transformer trained on more than hundred million synthetic datasets to perform in-context learning and output a predictive distribution for…

    Nov 2025 · priorlabs.ai · its alternatives →

  22. 22

    A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub

    2023 · github.com · its alternatives →

  23. 23

    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 &#x2F; 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…

    2025 · github.com · its alternatives →

  24. 24

    Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…

    2024 · github.com · its alternatives →

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