Open Agent Kit(OAK) Launch – Build Agents in Minutes

Hacker News

Hi Hackers OAK was built with a simple vision: to make AI integration effortless for developers and businesses. After a decade of building digital companies and agencies, we saw the struggles first-hand—fragmented tools, slow workflows, and rigid systems that made deploying AI feel like a daunting task. So, we set out to change that. With OAK, you get a platform designed to eliminate complexity. Our modular design, intuitive APIs, and seamless UI components empower your team to move from idea to production faster than ever. Whether you’re crafting a smart assistant or building an AI-powered customer experience, OAK gives you the flexibility and scalability to bring your vision to life—without the hassle. But we’re just getting started—and we want YOU to be part of it! Your feedback is invaluable as we continue to build and improve OAK. Join our community on Discord, share your thoughts, and help shape the future of AI integration. GitHub: https://github.com/byarcnine/ope ... NPM: https://www.npmjs.com/package/@o ... Developer Documentation: https://docs.open-agent-kit.com Discord: https://discord.gg/NqGpphH8Ge X: https://x.com/arc_nine_ai Together, let’s make AI integration effortless. Your ideas and feedback matter—jump in and shape the future with us!

Visit product View on Hacker NewsMar 26, 2025

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

77%77% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, intuitive · Missing: plus, reviews, host

38%38% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Incorrect prediction on native model

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