AI · alternatives · 2026
24 alternatives to Create LLM graders and run evals in JavaScript with one file
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…
bff-eval is a JavaScript tool that lets developers create LLM graders and run evaluations from a single file. It helps users assess the quality of AI assistant responses by grading outputs against specified criteria.… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
2023 · github.com · its alternatives →
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I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com · its alternatives →
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TensorZero▲307Hi 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 →
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LLM Stats▲293I 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/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Oct 2025 · llm-stats.com · its alternatives →
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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://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 →
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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 · its alternatives →
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I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations, latency, cost), you need to try various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, different agent architecture, and more. There are thousands of possible combinations and you need a process…
2024 · github.com · its alternatives →
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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 →
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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 · its alternatives →
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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 →
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APIEval-20▲121An open benchmark for AI agents that test APIs
May 2026 · resources.kusho.ai · its alternatives →
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Hey HN, Hakim here from Fini (YC S22), a startup focused on providing automated customer support bots for enterprises that have a high volume of support requests. Today, one of the largest use cases of LLMs is for the purpose of automating support. As the space has evolved over the past year, there has subsequently been a need for evaluations of LLM outputs - and a sea of LLM Evals packages have been released. "LLM evals" refer to the evaluation of large language models, assessing how well these AI systems understand and generate human-like text. These packages have recently relied on…
2024 · github.com · its alternatives →
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Hey all! @sridatta and I wrote a book/zine called Forest Friends on system evals for LLM-driven apps. But it's a bit more whimsical, a bit more visual, and very much inspired by the meme of LLMs being a shoggoth polished into a smiley face with RLHF. LLM system evals are important as companies move past the flashy AI demos to reliable production apps. System evals keep coming up as the answer for what you "should do", but it's not exactly a standard part of the software engineering toolkit. So we pulled from @sridatta's seven years as a research engineer at Google, plus a ton of best…
2024 · its alternatives →
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The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
2023 · github.com · its alternatives →
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I run a small AI lab and playground and got super excited about Anthropics paper "Verbalizable Representations Form a Global Workspace in Language Models" (https://transformer-circuits.pub/2026/workspace/index.html) It talks about how they use a tool they call a Jacobian Lens to view inside the middle layers of LLM while it's working before it commits to a word (token). I wanted to see if I could get a version of this running on the open models and to my surprise it worked! I ran some experiments with it and build a public facing free tool anyone can use with your…
Jul 2026 · lucid.earthpilot.ai · its alternatives →
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Code Mode▲144I’ve been testing a simple idea inspired by Apple/Cloudflare/Anthropic: LLMs are far better at writing a small program than coordinating multiple tool calls. So instead of giving the model 10+ tools, I exposed one: a TypeScript sandbox with access to the same interfaces. The model writes a script → it runs once → done. What changed - +68% reduction in token use - No multi-step drift or retries - Local models (Llama 3.1 8B / Phi-3) became much more reliable Repo: https://github.com/universal-tool-calling-protocol/code-mode Curious whether others have seen…
Nov 2025 · github.com · its alternatives →
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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/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/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 →
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I built this out of frustration as I lead the development of AI features at Yola.com. Prompt testing should be simple and straightforward. All I wanted was a simple way to test prompts with variables and jinja2 templates across different models, ideally somthing I could open during a call, run few tests, and share results with my team. But every tool I tried hit me with a clunky UI, required login and API keys, or forced a lengthy setup process. And that's not all. Then came the pricing. The last quote I got for one of the tools on the market was $6,000/year for a team of 16 people in a…
2025 · langfa.st · its alternatives →
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A test runner for agentskills.io-style AI agent skills - darkrishabh/agent-skills-eval
May 2026 · github.com · its alternatives →
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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…
Feb 2026 · ai-evals.io · its alternatives →
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