Persistent memory and grounded knowledge for AI agents — native MCP tools your agent calls directly. Drop in any file or URL; skip building and babysitting a RAG stack. One API, works with any model. Free to start.
Born from my own daily pain: my agent forgot everything I told it yesterday, half my knowledge lived in formats it couldn't read (PDFs, YouTube, docs sites), and the "fix" was babysitting a 5-service RAG stack. So I built it once, properly: any file/URL in clean, searchable knowledge out, plus remember/recall as native MCP tools. One install, any model.
Same problem everyone has "I wish my AI knew my stuff" solved as a drop-in layer.
The remember/recall-as-native-MCP-tools approach is what makes this feel usable versus babysitting a 5-service RAG stack. Two setup questions: is the knowledge store hosted by you, or can it run local/self-hosted so the data stays on my machine, and is memory scoped per-project so pointing it at two clients' docs can't bleed one into the other on recall? Also, when I drop in a URL, is that a one-time snapshot or does it re-crawl when the page changes?
One thing I'd love to see is a way to scope which agent or project owns a particular memory or knowledge doc, so different agents using the same Kit instance don't accidentally overwrite or pull each other's context.
Real talk 🙌 this is exactly what I needed. I've been struggling for a while with AI models forgetting context every single session, having to re-explain the same things over and over got exhausting . Kit For AI finally fixed that for me: I drop in a file or URL once, and my agent actually remembers it going forward, no extra setup, no complicated RAG pipeline. Huge thanks to the team for building something this simple and practical 🙏 it genuinely saves me hours every week.
Quick question though: how does the near-duplicate memory dedup work when the same fact gets updated over time, does it version the old memory or just overwrite it ?
How do you mitigate the inherent limitations of MCP when it comes to context window usage especially on large scale projects where even a million token context window seems minuscule?
honestly this looks super useful, been wanting something like this for ages. one thing though, it would be really helpful if there was a way to set automatic refresh intervals for ingested URLs so the knowledge stays current without having to manually re-upload stuff all the time
About Kit For AI on Product Hunt
“The memory layer for AI agents”
Kit For AI launched on Product Hunt on July 16th, 2026 and earned 82 upvotes and 8 comments, placing #23 on the daily leaderboard. Persistent memory and grounded knowledge for AI agents — native MCP tools your agent calls directly. Drop in any file or URL; skip building and babysitting a RAG stack. One API, works with any model. Free to start.
Kit For AI was featured in SaaS (43.1k followers), Artificial Intelligence (473.7k followers) and Vercel Day (26 followers) on Product Hunt. Together, these topics include over 158.2k products, making this a competitive space to launch in.
Who hunted Kit For AI?
Kit For AI was hunted by Aymen. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Kit For AI stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Aymen
Born from my own daily pain: my agent forgot everything I told it yesterday, half my knowledge lived in formats it couldn't read (PDFs, YouTube, docs sites), and the "fix" was babysitting a 5-service RAG stack. So I built it once, properly: any file/URL in clean, searchable knowledge out, plus remember/recall as native MCP tools. One install, any model.
Same problem everyone has "I wish my AI knew my stuff" solved as a drop-in layer.