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System architecture showing an AWS-powered AI agent routing user data through regional nodes connected by CockroachDB distributed SQL.
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OmniMind promotional banner highlighting high-performance cloud databases for agentic AI and globally distributed data architectures.
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Persistent memory console UI tracking contextual events, pinned preferences, and row-level TTL expiration countdowns for AI agents.
Inspiration
Modern AI agents often suffer from "amnesia" or rely on centralized, single-region databases that introduce high latency for global users. We wanted to build a globally distributed memory system where an AI agent can maintain long-term contextual recall across continents without compromising speed or data residency.
What it does
OmniMind is a globally distributed AI memory system. It uses an AWS-powered AI agent network that writes conversational context (user preferences, events, temporary states) directly to a unified CockroachDB cluster spanning multiple cloud regions.
It features a custom Persistent Memory Console UI that tracks two types of agentic memory:
- Pinned Memories: Core user preferences that persist permanently.
- Expiring Context: Temporary conversational states managed entirely by CockroachDB's Row-Level TTL (Time to Live), which automatically purges outdated context without heavy background sweeping.
How we built it
- Database: We utilized CockroachDB Serverless to act as our Distributed SQL engine, taking advantage of its multi-region survivability and native Row-Level TTL functionality.
- Cloud Infrastructure: The AI Agent processing layer is hosted on AWS across
us-eastandeu-centralregions to ensure low-latency interactions globally. - Backend & Frontend: The orchestration layer is built with Python/Flask, serving a sleek, dark-mode desktop UI constructed with HTML5, CSS3, and JavaScript.
Challenges we ran into
Orchestrating state synchronization across multi-region AWS deployments while ensuring CockroachDB's TTL configurations seamlessly aligned with our Python agent's logic was tricky. We had to carefully structure our SQL schema to ensure the TTL expirations didn't prematurely wipe out critical conversational context mid-session.
Built With
- ai
- amazon-web-services
- cockroachdb
- css3
- distributed-sql
- flask
- html5
- javascript
- python
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