Walrus Memory enables AI agents to operate reliably across apps and sessions, without losing context. Portable, verifiable, and fully controlled by you, it is the memory layer that lets agents handle complex workflows and coordinate using data they can trust.
Hey Product Hunt 👋,
I’m Abner, Community lead at Walrus Foundation.
If you’re building AI agents, you’ve probably run into the same problem: a session ends and the context goes with it. Agents forget what they’ve learned, repeat work, and start from zero more often than they should.
Most teams end up stitching together memory from databases, vector stores, application logic, and permissions. It works — until you need agents to share context, coordinate reliably, or carry state across tools and sessions.
That’s why we built Walrus Memory.
It’s a portable memory layer for AI agents. Wire in two calls: remember and recall. Your agents keep context, share knowledge, and build on previous work instead of resetting every time.
The goal is simple: memory that holds up in production.
We’re curious how others are approaching this problem.
How are you handling memory today? What’s been the hardest part? I'd love to hear your feedback in the comments.
A lot of memory systems seem to work well when retrieving facts but struggle once the underlying reality changes.
How are you thinking about state changes over time? For example, if an agent learns a preference today and then learns an updated preference next week, is the challenge primarily retrieval or maintaining a correct current view of state?
The part I’d be most curious about is the “trust boundary” around memory writes.
For agents, remembering everything is almost as risky as forgetting everything. I’d want memory to keep a trail of: who/what wrote it, source, confidence, freshness, and whether it’s a preference vs. a fact vs. a temporary project decision.
If Walrus makes that inspectable for users and agents, it becomes much easier to rely on memory in production instead of treating it like a black box.
Agents are getting better, but without memory across sessions and apps, the user still has to do too much of the thinking and re-explaining. I like the “portable and controlled by you” angle. For memory, trust matters as much as performance.
What’s the main use case today: coding agents, research workflows, or cross-app task execution?
About Walrus Memory on Product Hunt
“Enable agents to keep context & work across apps + sessions”
Walrus Memory launched on Product Hunt on June 4th, 2026 and earned 83 upvotes and 8 comments, placing #27 on the daily leaderboard. Walrus Memory enables AI agents to operate reliably across apps and sessions, without losing context. Portable, verifiable, and fully controlled by you, it is the memory layer that lets agents handle complex workflows and coordinate using data they can trust.
Walrus Memory was featured in Developer Tools (514k followers), Artificial Intelligence (471k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 195k products, making this a competitive space to launch in.
Who hunted Walrus Memory?
Walrus Memory was hunted by Abner Pinto. 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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Abner Pinto