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MemoryLayer
Local-first AI memory with reasoning and formal verification
Not a vector database. MemoryLayer reasons over what it's told, catches contradictions, derives facts you never stated, and checks every claim before your agent repeats it. Local-first, MCP-native. One command and you're live.
Hey PH! I'm Raaj, the maker of MemoryLayer.
I built this because I kept hitting the same wall: my AI coding agent would answer the same question differently in every session, hallucinate a function signature it had already learned was wrong, or suggest a change without knowing what else it would break. The problem wasn't the model - it was that there was no persistent, reasoning layer between sessions.
Most memory tools are just vector DBs. They find similar text and return it. MemoryLayer does something different: it extracts subject-verb-object triples from everything you tell it, runs them through a biologically inspired reasoning engine that catches contradictions, derives facts you never explicitly stated, and then uses Z3 formal logic to verify claims before repeating them. When it doesn't know, it says so instead of guessing.
It's local-first - your memories never leave your machine. It drops into Claude Code, Cursor, and Windsurf via one MCP config line. No server to run, no account required for the free tier.
The registry at registry.memorylayer.in lets you publish memory packages (.memorypkg files) so teams can share domain knowledge - think npm but for your agent's institutional memory.
I'd love to hear: what's the most painful memory failure you've hit with AI coding tools? That'll help me know what to build next.
Install: npm install -g @raajvamsy/memorylayer && memorylayer setup
Try the live demo: memorylayer.in/arena
spun it up locally and the contradiction catching actually works, like I fed it conflicting info on purpose and it flagged it right away. kind of wild that it derives facts too, that part genuinely surprised me.
The local-first, MCP-native stance is genuinely rare in this space, and the contradiction-catching angle is what sold me. Most memory tools just stuff everything into a pile and hope for the best.
Spun it up locally and honestly the contradiction catching caught me off guard, it flagged something I genuinely didn't realize was inconsistent in my own notes. The local-first setup is genuinely one command, which I appreciate.
About MemoryLayer on Product Hunt
“Local-first AI memory with reasoning and formal verification”
MemoryLayer was submitted on Product Hunt and earned 0 upvotes and 4 comments, placing #53 on the daily leaderboard. Not a vector database. MemoryLayer reasons over what it's told, catches contradictions, derives facts you never stated, and checks every claim before your agent repeats it. Local-first, MCP-native. One command and you're live.
MemoryLayer was featured in Productivity (656.3k followers), Developer Tools (515.9k followers) and Artificial Intelligence (473.8k followers) on Product Hunt. Together, these topics include over 330.4k products, making this a competitive space to launch in.
Who hunted MemoryLayer?
MemoryLayer was hunted by Raaj Vamsy. 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.
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Raaj Vamsy