Walrus Sessions 8: Building chatbots that actually remember you

Most chatbots forget everything the second a conversation ends. Ask it something on Monday, come back Tuesday, and you're a stranger again. Walrus Sessions 8: "Chatbots That Remember" is a live hackathon session tackling exactly that - building (or retrofitting) a chatbot that uses Walrus Memory to persist context across sessions, users, and devices.

The Challenge

The goal is to build or retrofit a chatbot that:

• Stores and recalls context with Walrus Memory (on mainnet)
• Gets deployed somewhere real and used for a few days
• Is written up - what it does, how memory was integrated, before/after, real evidence it works

Any use case counts: customer support, onboarding, tutoring, sales, community bots, even game NPCs.

Why This Matters

I spent a few days going deep on the underlying open-source SDK (MemWal) - not just building with it, but actually reading through the codebase: the auth layer, the on-chain contract, the sample apps. It's a genuinely well-built system (constant-time auth checks, on-chain delegate-key verification, careful quarantine/replay protection). Digging into how persistent memory is implemented securely taught me more than most tutorials do. I ended up finding and reporting a couple of real bugs along the way, which the maintainers fixed fast.

Event Details

• Dates: Sept 18 - Oct 9, 2026
• Prizes: $2,500 in WAL total
• Best Chatbot: $500 / $250 / $150
• Beyond the Big Two (non-Claude/GPT): 2 × $150
• Best Article: 3 × $100
• Bug Bounty: 5 × $100 (no chatbot required, just a reproducible bug on GitHub)
• Repo: https://github.com/MystenLabs/MemWal
• Full rules & submission: https://walform.wal.app/f?formId=0x09b022796f9cb7ce24247e3097c5c8ae2b414317c90c8aeb6ce335e7caf31ff5

If you've ever wanted an excuse to actually understand how persistent, encrypted agent memory works under the hood instead of just calling an API - this is a good one to dig into, whether you build the full chatbot or just go bug-hunting in the SDK.