Project Potemkin
AI Agent & Memory Systems
Project Potemkin is an experimental AI agent system focused on persistent memory, autonomous interaction, and long-term user relationships.
Overview
This project was our submission for UK AI Agent Hackathon EP3 by ASI (Artificial Superintelligence Alliance), one of the largest Web3 x AI hackathons in Europe.
From first idea to final demo and promo video, we pushed it out in one week. For me, this project was a big turning point because it pulled me into Web3 while forcing me to learn a lot of web development in real time.
The core concept is autonomous AI behavior plus on-chain memory ownership, so users keep sovereignty over their memory agent and can still support decentralized third-party sharing. It is still evolving, but we genuinely believe the direction is worth building. You can watch the trailer below and try the demo directly.
Project Partners
Built with: Zhewen (Ryan) Zhen, Shian (Andy) Ye.
Key Ideas
- Persistent memory architecture
- Autonomous agent behavior
- Human-AI interaction over time
- Multi-session context evolution
Implemented Highlights
- Family-scoped identity with isolated memory storage per family.
- Automatic long-term memory + profile refresh every 16 short-term turns.
- Memory buckets: shared, important events, private, and user-approved public.
- Signup/login, bearer token auth, and multi-member family accounts.
- Family-level language setting with multilingual input support.
- Optional BNB Testnet on-chain space and permission purchase flow.
Memory & Profile Flow
- Each turn is written to STM (SQLite + vector retrieval store).
- Every 16 turns are summarized into LTM automatically.
- Family profile is refreshed after long-term summarization.
- Optional hub sync enables cross-device memory reuse.
Demo & Links
Technologies
Python · LLM APIs · Memory Systems · Agent Architecture
Project Trailer
Project Potemkin introduction video.