Chatbots That Remember

About

Any LLM or runtime is welcome, including GPT, Claude, Gemini, Llama, Mistral, Qwen, DeepSeek and locally hosted models. Free to enter, with $2,500 in total rewards across multiple categories. No previous experience with Walrus Memory is required. Most chatbots forget you the moment the conversation ends. A support bot asks for your order number for the third time. A companion app has no idea what you told it yesterday. An internal helpdesk bot re-explains the same fix to the same person every week. This session is about fixing that. Build a chatbot that uses Walrus Memory to remember across sessions, across users, and across devices, and show what changes when it does. Customer support is the obvious use case and a great place to start, but any chatbot counts: website support bot, onboarding assistants, internal IT/HR helpdesks, sales and lead-qualification bots, tutoring bots, community moderators, personal assistants, companion or coaching bots, game NPCs. If it talks to people and it should remember them, it qualifies. What You Need to Do Build a chatbot that uses Walrus Memory to persist and recall context between conversations, make sure it can learn and adapt to the individual. It can be new or an existing bot you retrofit with memory. Deploy it somewhere real (a website widget, Telegram, Discord, WhatsApp, Slack, a CLI, anything people can actually talk to) and use it, or have others use it, for at least a few days before you write about it. - Then write about what happened. - One submission per person or team. - Walrus Memory needs to be integrated in to the solution - Showcase at least 3 different users which are using the chat agent and storing at least 10 memories each. - Your submission is made through the form (links below). https://airtable.com/appoDAKpC74UOqoDa/shro5iVzzjoWfZlPK You will be asked for: --Which LLM / runtime you built with (e.g. Claude, GPT, Gemini, Llama via Ollama, Mistral, Qwen, DeepSeek, etc.) --Link to your public GitHub repo (open source, with setup instructions) --bug or friction point and/or improvement idea for Walrus Memory --Link to your published article on Medium or Inkray (see below; this is where you explain the build) --Link to your X post The Article The article is the heart of your submission. Publish it on Medium or Inkray (or both), and share it on X tagging @WalrusProtocol under the session announcement using #WalrusMemory. Your article must cover: - What your chatbot does, who it is for, and the problem it solves - How you integrated Walrus Memory: what gets stored, when it is recalled, how it shapes responses - A before/after: how the bot behaved without memory vs. with it, and what changed for the people talking to it - Evidence of real use: screenshots, conversation logs, a video, or a live link showing the bot remembering across sessions Write it as your own story: the problem you were trying to solve, how you wired in Walrus Memory, what broke along the way, and what the bot did differently once it had memory. Show real conversations, especially the moment the bot recalled something from a previous session and it actually mattered. Be specific. Someone searching for “how to add memory to a support chatbot” or “chatbot that remembers users between sessions” should find your article and immediately see themselves in it. Write the title and opening with that person in mind. Honest over polished. Around 500-800 words. Judging Submissions are judged by the Walrus team on: - Does it actually remember? Is memory doing real work, or is it decorative? Does the bot recall the right things at the right time, and does that visibly improve the conversation? - Real-world use. Was it deployed and used by real people (even a handful)? Is the before/after convincing? Does the evidence show memory made a genuine difference? - Build quality. Is the integration clean, documented, and reproducible? Could someone clone the repo and run it? - The article. Is it useful to someone who has never used Walrus Memory? Could they follow it? Does it make them want to build one themselves? Prizes Main Prizes: Best Chatbot ($900 total in stablecoin) - Selected by the Walrus team. The three best overall submissions, judged on all criteria above. Submit here: https://airtable.com/appoDAKpC74UOqoDa/shro5iVzzjoWfZlPK 1st place: $500 worth of stablecoin 2nd place: $250 worth of stablecoin 3rd place: $150 worth of stablecoin Beyond the Big Two: Open & Alternative Models ($300 stablecoin total) Almost everyone builds with Claude or ChatGPT. We want to see Walrus Memory working everywhere else, and find the bugs that only show up when you leave the beaten path. This category is for chatbots whose primary LLM is not from Anthropic or OpenAI. That includes: - Local / self-hosted open-weight models (Llama, Mistral, Qwen, Gemma, DeepSeek, Phi, etc.) running via Ollama, LM Studio, llama.cpp, vLLM, or similar - Other hosted providers (Google Gemini, xAI Grok, DeepSeek, Cohere, Groq, Together, Fireworks, OpenRouter, etc.) - Fine-tuned or custom models of your own To qualify, your submission must clearly state in the article which model and runtime you used and document any integration friction you hit with Walrus Memory in that environment. Bugs you surface here are also eligible for the Bug Bounty below. - 2 winners, $150 worth of stablecoin each A submission can win in both this category and the Main Prizes. Best Article ($300 stablecoin total) The three best written stories about how you built your chatbot and how Walrus Memory changed it. Judged on clarity, honesty, and usefulness to someone who has never used the tool. - 3 winners, $100 worth of stablecoin each Bug Bounty ($500 stablecoin total) Report reproducible bugs or friction points in Walrus Memory via GitHub Issues at https://github.com/MystenLabs/MemWal/issues. Include steps to reproduce, expected vs. actual behaviour, and your environment (model, runtime, OS, SDK version). If you are only submitting a bug bounty, without the chatbot build, submit here: https://walform.wal.app/f?formId=0x38a736485349b133604c1caf286d669b4b774d16f0a26120ce839cad245baeef - 5 winners, $100 worth of stablecoin each Selected separately by the Walrus engineering team. Bug Bounty winners are chosen independently of the other categories. Promo Prize ($500 worth of stablecoin total) Help spread the word. Post about the session, either by announcing it or by sharing your article, in a community outside the Walrus and Sui ecosystem: a relevant subreddit, a developer forum, or similar. Posts on X, r/sui, or Walrus and Sui channels do not count. If you are making a full submission, add the link in your submission form. If you only want to promote and not build, use the promo-only form below. - 5 winners, $100 worth of stablecoin each Promo only form: https://walform.wal.app/f?formId=0x09b022796f9cb7ce24247e3097c5c8ae2b414317c90c8aeb6ce335e7caf31ff5 Links - Walrus Memory GitHub: https://github.com/MystenLabs/MemWal - Walrus Discord: https://discord.com/invite/walrusprotocol - Chatbot example: https://docs.wal.app/walrus-memory/examples/chatbot If you are new to Walrus Memory, learn more in this blog: https://blog.walrus.xyz/how-to-add-portable-memory-to-claude-code-and-codex-with-walrus-memory/ You can find more links and a presentation here: https://app.notion.com/p/mystenlabs/SuiHub-Lagos-Walrus-Memory-Hackathon-1-3756d9dcb4e9808ca16fc8c22562e3c6