Yet another LLM agent framework but minimal and just TypeScript

Hacker News

Most LLM agent frameworks feel like they were designed by a committee - either trying to solve every possible use case with convoluted abstractions or making sure they look great in demos so they can raise millions. I wanted something lightweight, code-first, and TypeScript-native, so I built a new one. It’s a minimal, strongly-typed agent framework that lets you structure LLM-powered apps without unnecessary complexity. Key ideas: Minimal – No unnecessary complexity, just the essentials Code-first – Feels like normal TypeScript, not a magic black box Type-first – Structured inputs/outputs using Zod/@annotations Explicit control – You define how agents behave, no hidden surprises Model-agnostic – OpenAI, Anthropic, DeepSeek, etc. Links: GitHub: https://github.com/axar-ai/axar Docs: https://axar-ai.gitbook.io/axar Would love to hear thoughts, especially if you think this approach is flawed.

Visit product View on Hacker NewsJan 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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