Alternatives
Products that do what RΞASON – Open-source TypeScript framework for LLM apps does
Hi HN! I'm Inacio, the author of REASON. I've been tinkering with LLMs since the GPT-3 API's release May 2020. Seeing the emergence of many frameworks in the last 15 months has been exciting. While many understandably focused on launching quick, I found myself intrigued by a different question: what would a thoughtful, first-principles approach look like? This curiosity led to a personal project, RΞASON, a minimalistic open-source TypeScript framework. It's been a couple of months of digging deep and learning a ton, and I'm eager to share it and grow it with insights from all of you. In the…
- 1RO
Hi HN! RΞASON is an OSS Typescript framework for developing LLM apps that uses Typescript's interfaces to get structured output from an LLM. While there are other TS LLM frameworks, I think RΞASON fills a unique space in the market: it's laser-focused on only three areas and, most importantly, actively stays away from pre-made prompting & retrieval. I've been in the LLM space since GPT-3 originally came out, and I've always had problems with other frameworks, such as LangChain. I dislike that they focus a ton on out-of-the-box prompting & pre-made agents — I, as the dev, should be the one in…
2023 · github.com
- 2AO
Hi HN, we are Nick and Ben, creators of Axilla - an open source TypeScript framework to develop LLM applications. It’s in the early stages but you can use it today: we’ve already published 2 modules and have more coming soon. Ben and I met while working at Cruise on the ML platform for self-driving cars. We spent many years there and learned the hard way that shipping AI is not quite the same as shipping regular code. There are many parts of the ML lifecycle, e.g., mining, processing, and labeling data and training, evaluating, and deploying models. Although none of them are rocket science,…
2023 · github.com
- 3IW
Hey HN, I made Browser-Use, an open-source tool that lets (all Langchain supported) LLMs execute tasks directly in the browser just with function calling. It allows you to build agents that interact with web elements using natural language prompts. We created a layer that simplifies website interaction for LLMs by extracting xPaths and interactive elements like buttons and input fields (and other fancy things). This enables you to design custom web automation and scraping functions without manual inspection through DevTools. Hasn't this been done a lot of times? Good question, as a general…
2024 · github.com
- 4NO
Hello HN! The day has finally come to stop adding features and start sharing what I've been building the last 5-6 months. It's a bit of CrewAI, OpenDevon, LangFuse/Cloud all in one, providing devs who prefer TypeScript an integrated framework thats provides a lot out of the box to start experimenting and building agents with. It started after peeking at the LangChain docs a few times and never liking the example code. I began experimenting with automating a simple Jira request from the engineering team to add an index to one of our Google Spanner databases (for context I'm the…
2024 · github.com
- 5WW
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
- 6OS
Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.
2025 · github.com
- 7AJ
Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…
2025 · github.com
- 8OA
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
- 9CA
We've been building Crust (https://crustjs.com/), a TypeScript-first, Bun-native CLI framework with zero dependencies. It's been powering our core product internally for a while, and we're now open-sourcing it. The problem we kept running into: existing CLI frameworks in the JS ecosystem are either minimal arg parsers where you wire everything yourself, or heavyweight frameworks with large dependency trees and Node-era assumptions. We wanted something in between. What Crust does differently: - Full type inference from definitions — args and flags are inferred automatically. No…
Mar 2026 · github.com
- 10LA
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
- 11RL
We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com
- 12OS
Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
2024 · getindexify.ai
- 13BR
Check out this impressive project that enables running LLMs entirely in the browser using WebGPU. Key features: - Zero token costs, no cloud infrastructure required - Complete data privacy through local processing - Simple 3-line code integration - Built on MLC and Transformer.js The benchmarks show smaller models can effectively handle many common tasks. Currently the project roadmap includes: - No-code AI pipeline builder - Browser-based RAG for document chat - Analytics/logging - Model fine-tuning interface
2025 · github.com
- 14AO
Hi Hacker News! My name is Sagar, I’m working on a startup called Speakeasy - we’re making all APIs self-service. The platform is currently in beta, but we’re independently launching this tool which you can use to generate language-idiomatic, statically-typed TS SDKs from any public OpenAPI schemas. We hope to continue iterating on this to give devs a way to easily generate high fidelity client SDKs for all the major languages. Inspiration for this product is from past experiences struggling with OpenAPI. I was originally optimistic about using the OpenAPI tools to build out our offering,…
2022 · easysdk.xyz
- 15OO
Hey HN, we're super excited to share something we've been working on: OpenLIT. After an engaging preview that some of you might recall, we are now proudly announcing our first stable release! *What's OpenLIT?* Simply put, OpenLIT is an open-source tool designed to make monitoring your Large Language Model (LLM) applications straightforward. It’s built on OpenTelemetry, aiming to reduce the complexities that come with observing the behavior and usage of your LLM stack. *Beyond Basic Text Generation:* OpenLIT isn’t restricted to just text and chatbot outputs. It now includes automatic…
2024 · github.com
- 16SO
We built SwiftAI, an open-source Swift library that lets you use Apple’s on-device LLMs when available (Apple opened access in June), and fall back to a cloud model when they aren’t available — all without duplicating code. SwiftAI gives you: - A single, model-agnostic API - An agent/tool loop - Strongly-typed structured outputs - Optional chat state Backstory: We started experimenting with Apple’s local models because they’re free (no API calls), private, and work offline. The problem: not all devices support them (older iPhones, Apple Intelligence disabled, low battery, etc.). That…
2025 · github.com
- 17ZD
Hey HN! We just released a new library for building LLM-powered applications: @axflow/models. It is part of a larger suite of libraries we're developing for TypeScript developers working with generative AI. This library provides the simplest APIs for 1) invoking the most popular LLM and embedding models (openai, anthropic, cohere, huggingface, etc.) 2) streaming LLM responses to clients, including augmenting the streams with additional arbitrary data and 3) building client-side applications with React hooks. @axflow/models has zero dependencies and is built using only the…
2023 · docs.axflow.dev
- 18MG
Hello HN, I've been working on this project for a while, and it has been in an "open" beta for some time. I finally believe it's ready for its first release. I hope you like it. Here are some potential questions that may arise: 1. How does it compare to LM Studio? It's likely that if you're already using LM Studio, you'll continue to do so. This project is designed to be more user-friendly. 2. Is it open-source? No, it is not. 3. Does it use any open-source libraries? Yes, it uses llama.cpp and a few others, as indicated in the license information included with the application. 4. Why is not…
2023 · avapls.com
- 19AR
Hey HN, I wanted to share a UI toolkit project I’ve been working on recently, born out of how difficult I found it to build a great UX on top of LLMs, and keep application state in sync. I’ve built: - A React/JS front-end library for conversational interfaces, which makes it super easy to bootstrap AI assistants and ChatGPT style UX: https://github.com/nlkitai/nlux - A set of adapters that simplify integration with AI backends such as LangServe and HuggingFace The library is highly configurable, easy to theme, supports markdown streaming (that was tough to get…
2024 · github.com
- 20AJ
I've been building a web UI library for a side project of mine. I thought it might be useful to others, so I'm releasing it as open source. To put it simply, I realized that most of my pain points with React come from its declarative model ui=f(state). So I'm trying something that I'm calling "imperative JSX." Instead of treating JSX as the source of truth for your UI, it essentially becomes a query interface for DOM manipulation. I first had the idea for it a few months ago, and only began writing it in earnest last week, so it's extremely early and nowhere near production-ready. Still, I'd…
2024 · npmjs.com
- 21AL
Hi HN, I build this a few months back, thinking about improving it but wanted to share as is first to see what feedback I can get. I built it to be modular and with a clean understandable codebase in mind so easily extendable. I want to add more components to it maybe... Thanks! Will add link to the repo in a comment now
2022
- 22OS
Hi HN, we’re Dylan and Matthew, building sublingual (https://github.com/sublingual-ai/sublingual), an open-source LLM observability tool you can use with zero code changes. As developers focused on iterating and building features as fast as possible, we felt observability would’ve been a helpful tool to have, but we found existing solutions had too much overhead to set up. So we gave ourselves the challenge of building an observability tool that you can integrate without changing a single line of code in your project. How it works Run your python application as usual with…
2025 · github.com
- 23KA
Knit was created to solve pains of other LLM playgrounds. Some of the highlights: - Smart prompt builder, create prompt with simple requirement and few shot learning, fast and effortlessly. - Function call simulation, visualize the function callings and you can also setup a mocked value to return. - Support OpenAI/Anthropic/Azure models. - Manage prompts with projects and members. - And so much more! I have been developing Knit by myself for over 4 months now, and am looking for ways to improve it. Any feedback is appreciated.
2023 · promptknit.com
- 24SD
2019 · github.com
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