What if a chatbot could actually remember you?
Most chatbots forget everything once a conversation ends. You might tell a support bot your order number, explain your problem, come back the next day, and have to start the entire conversation again.
That's exactly the problem Walrus Memory is trying to solve.
The Walrus Session 8: Chatbots That Remember campaign challenges developers to build chatbots that can remember useful information across conversations, users, and devices.
And there is a $2,500 prize pool across multiple categories.
What is Walrus Memory?
Walrus Memory is designed to give AI agents persistent memory.
Instead of treating every conversation as completely independent, an application can store useful information about users and later retrieve relevant memories when they are needed.
The idea looks simple:
Conversation 1
↓
Useful information is remembered
↓
Conversation ends
↓
User returns later
↓
Relevant memory is recalled
↓
Better, more personalized response
This can make an AI assistant feel much more useful because it doesn't have to rediscover the same context every time.
You can learn more from the official Walrus Memory project:
https://github.com/MystenLabs/MemWal
The challenge: Chatbots That Remember
The campaign asks developers to build a chatbot that uses Walrus Memory to persist and recall context between conversations.
The chatbot can be almost anything.
Some example ideas include:
- Customer support bots
- Website assistants
- Onboarding assistants
- Internal IT or HR helpdesks
- Sales and lead-qualification assistants
- Tutoring bots
- Community moderators
- Personal assistants
- Companion or coaching bots
- Game NPCs
The important requirement is simple:
If it talks to people and should remember them, it qualifies.
You can either create a completely new chatbot or take an existing chatbot and add persistent memory to it.
What participants need to do
The campaign is not just about creating a demo.
Participants are expected to deploy the chatbot somewhere people can actually use it.
For example:
Website
Telegram
Discord
WhatsApp
Slack
CLI
The chatbot should then be used for at least a few days before the participant writes about the experience.
The challenge specifically asks participants to showcase:
At least 3 different users
and
At least 10 memories stored for each user.
This is important because the goal is to demonstrate that memory is genuinely being used rather than simply displaying a memory feature in the interface.
What should be stored?
The exact implementation is up to the developer, but useful memories could include:
User preferences
Previous conversations
Past problems and solutions
Ongoing tasks
Personal context
Frequently requested information
Long-term goals
The key is relevance.
A good memory system shouldn't simply save everything. It should help the chatbot retrieve the information that actually matters to the current conversation.
The article is an important part of the submission
Participants also have to publish an article on Medium or Inkray and share it on X while tagging @WalrusProtocol and using:
#WalrusMemory
The article should explain:
1. What the chatbot does
Explain the use case, target users, and problem being solved.
2. How Walrus Memory is integrated
Show what information is stored, when memory is recalled, and how that memory affects the chatbot's responses.
3. Before vs. after
This is one of the most interesting parts of the challenge.
Show how the chatbot behaves without memory compared with with memory.
For example:
WITHOUT MEMORY
User: I'm having the same problem again.
Bot: Can you explain the problem and provide your details?
WITH MEMORY
User: I'm having the same problem again.
Bot: Welcome back. Last time we identified this as
a deployment configuration issue. Is this the same problem?
The second experience is what persistent memory is supposed to enable.
4. Evidence
Participants should include real evidence such as:
- Screenshots
- Conversation logs
- A video
- A live chatbot
- Examples of cross-session memory
The strongest submissions will show a moment where the chatbot remembers something from a previous conversation and that memory genuinely improves the interaction.
Which AI models can be used?
Developers have a lot of freedom here.
The campaign allows different LLMs and runtimes, including examples such as:
Claude
GPT
Gemini
Llama
Mistral
Qwen
DeepSeek
There is also a dedicated category for developers who choose models outside Anthropic and OpenAI.
That includes local or self-hosted models such as:
Llama
Mistral
Qwen
Gemma
DeepSeek
Phi
running through tools and runtimes such as:
Ollama
LM Studio
llama.cpp
vLLM
Other hosted providers are also eligible.
The $2,500 prize pool
This campaign isn't limited to one winner.
There are several different ways to win.
🏆 Best Chatbot — $900 total
The three strongest overall submissions are selected by the Walrus team.
1st place: $500 in stablecoin
2nd place: $250 in stablecoin
3rd place: $150 in stablecoin
Judging is based on the overall quality of the submission, including memory usage, real-world impact, build quality, and the article.
🤖 Open & Alternative Models — $300 total
This category is for chatbots whose primary model is not from Anthropic or OpenAI.
There are:
2 winners × $150
Projects can also win in this category and the main Best Chatbot category.
✍️ Best Article — $300 total
The three best-written stories about building a chatbot and using Walrus Memory can win:
3 winners × $100
The judges are looking for articles that are clear, honest, useful, and easy for another developer to understand.
🐛 Bug Bounty — $500 total
Developers can also find and report reproducible bugs or friction points in Walrus Memory.
There are:
5 winners × $100
Reports should include things such as:
Steps to reproduce
Expected behavior
Actual behavior
Model/runtime
Operating system
SDK version
Bug bounty submissions are judged separately from the main chatbot competition.
📣 Promo Prize — $500 total
There is also a prize for helping spread the campaign outside the Walrus and Sui ecosystem.
There are:
5 winners × $100
For example, participants can publish the campaign or their article in a relevant developer community, subreddit, or forum.
Posts on X, r/sui, or Walrus/Sui channels don't qualify for this particular prize.
How submissions are judged
The judging criteria focus on four major areas.
Does the chatbot actually remember?
The memory should be useful.
The bot should recall the right information at the right time and use it to improve the conversation.
Was it actually used?
Real users and real conversations matter.
A working chatbot with evidence of actual usage is much stronger than a simple screenshot of a prototype.
Is the build good?
The integration should be clean, documented, and reproducible.
Someone should be able to look at the GitHub repository and understand how to set it up.
Is the article useful?
The article should teach someone who has never used Walrus Memory what it is, how it works, and why they might want to build with it.
Why this campaign is interesting
The most interesting part of this challenge isn't simply the prize money.
It's the direction AI assistants are moving toward.
Today's AI systems are increasingly good at generating answers, but an assistant becomes much more useful when it can maintain meaningful context over time.
Imagine a support assistant that remembers your previous issues.
A tutoring bot that remembers what topics you struggle with.
A sales assistant that remembers what a potential customer is interested in.
A personal assistant that remembers your preferences.
A game character that remembers previous interactions with a player.
These experiences become possible when memory becomes part of the AI architecture rather than something limited to one conversation.
Useful Walrus Memory resources
Official GitHub repository:
https://github.com/MystenLabs/MemWal
Walrus chatbot example:
https://docs.wal.app/walrus-memory/examples/chatbot
Walrus Discord:
https://discord.com/invite/walrusprotocol
Walrus Memory introduction:
https://blog.walrus.xyz/how-to-add-portable-memory-to-claude-code-and-codex-with-walrus-memory/
Final thoughts
The Walrus “Chatbots That Remember” campaign is essentially a challenge to move beyond stateless chatbots.
Instead of:
“Hello, how can I help you?”
every time a user returns, the goal is to build assistants that can say:
“Welcome back. I remember what we were working on.”
That's the core idea behind Walrus Memory.
With a $2,500 total prize pool, multiple award categories, support for different AI models, and a strong focus on real-world usage, the campaign gives developers several ways to participate.
And perhaps the most interesting question isn't:
“Can an AI remember?”
It's:
“What becomes possible when it does?”
Build it. Deploy it. Test it with real users. Show the difference.
And make your chatbot remember.
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