By Nidhi: Built for the Global AI Hackathon Series with Qwen Cloud, Track 1: MemoryAgent
When I signed up for Track 1, I figured memory was basically a vector database problem. Store embeddings, retrieve similar messages, done. The first prototype worked fine. Then I started actually trying to use it, and that's when things got interesting.
The idea
Most AI chat is stateless. Close the tab, and the next conversation starts from zero. That's a strange limitation for something we keep calling "intelligent": a person who knew you well wouldn't forget your name, your job, or what you told them you were working on, three days ago. So the plan was: build a memory layer underneath a chat agent. Every turn would recall relevant past context, respond with it in mind, and extract new facts worth remembering. Important things stick around. Trivial things expire.
That part came together quickly. FastAPI for the backend, split into two services: a memory core handling storage and intelligence, and a thin chat layer on top calling into it. Qwen Cloud for both the chat model and the embeddings. Neon Postgres with pgvector for the actual vector storage, Upstash Redis for caching, all of it deployed on Alibaba Cloud ECS. Within a couple of days I had a working chat UI that stored memories, scored them for importance, and recalled them on the next message.
