Alternatives
Products that do what evaligo does
Evaluate, compare, and deploy AI prompts in seconds.
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Hey HN! We’re Kevin and Steve. We’re building PromptTools (https://github.com/hegelai/prompttools): open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. Evaluating prompts, LLMs, and vector databases is a painful, time-consuming but necessary part of the product engineering process. Our tools allow engineers to do this in a lot less time. By “evaluating” we mean checking the quality of a model's response for a given use case, which is a combination of testing and benchmarking. As examples: - For generated…
2023 · github.com
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Hi friends, We are building EVA, an AI-Relational database system with first-class support for deep learning models. Our goal with EVA is to create a platform that supports AI-powered multi-modal database applications operating on structured (tables, feature vectors, etc.) and unstructured data (videos, podcasts, pdf, etc.) with deep learning models. EVA comes with a wide range of models for analyzing unstructured data, including models for object detection, OCR, text summarization, audio speech recognition, and more. The key feature of EVA is its AI-centric query optimizer. This optimizer…
2023 · github.com
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Hey HN, We're excited to introduce Braintrust, a platform for running and tracking AI evaluations (“evals”) [1]. At my previous startup Impira and leading AI at Figma, we had this recurring problem where we never knew if changes we made to our products would improve or regress key user scenarios. We built some tooling to solve this problem and after talking to other developers learned that it was a widespread issue. Specifically, it’s challenging to establish a great dev loop that lets you systematically improve and ship high quality AI products. We worked with the teams at Zapier, Coda, and…
2023
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May 2026 · github.com
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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…
2025 · github.com
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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!
2023 · github.com
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Hey HN! We built EvalKit, a library you embed to capture agent actions and a UI where domain experts give feedback, evaluate and improve AI agents. We experienced, in large agentic systems, prompt-engineering or auto-prompt improvement tool can get accuracy from 0 to 50% but for increasing accuracy to 100% we had to work with domain experts. Example -> In a law ai agent, lawyers are needed because law is complex and lawyers have a deeper context compared to non-lawyers. Other evaluation tools in the market focus on the experience of the developer and we are focusing on making as easy as…
2025 · github.com
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