Kage, verification and freshness for Google's OKF agent memory

Hacker News

Kage was always a document format memory with it's own memory standards... and was betting on file based memory + git native. It's good to see that Google also thinks the same and released OKF(Open Knowledge Format) and Kage has adopted OKF with open arms. Though Google has released the Memory standard and how to structure the memory, it doesn't do verification, when and how memories are created. That's where Kage comes in, Kage as a framework works with your agent, understand what to save, when to save, how to save, it also help the agent to recall relevant memory/maintain it's freshness. Kage is focused on maintaining your repo's memory for you and give you best experience when coordinating and working with teammates on the same repo. Just install Kage and let you agents do the memory maintenance job itself using Kage. Best support with Claude Code(Hook), also available and works with all the over coding agents.

Visit product View on Hacker NewsJun 30, 2026

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

94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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

20%20% 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

17%17% 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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