I don't see the advantage over a simple text file accessible by MCP. Could you elaborate?
replies(2):
The deeper problem
1. Not portable – context is vendor-locked; nothing travels across tools.
2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance.
3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried.
Demo video: https://youtu.be/iANZ32dnK60
Repo: https://github.com/RedPlanetHQ/core
What we built
- CORE (Context Oriented Relational Engine): An open source, shareable knowledge graph (your memory vault) that lets any LLM (ChatGPT, Cursor, Claude, SOL, etc.) share and query the same persistent context.
- Temporal + relational: Every fact gets a full version history (who, when, why), and nothing is wiped out when you change it—just timestamped and retired.
- Local-first or hosted: Run it offline in Docker, or use our hosted instance. You choose which memories sync and which stay private.
Try it
- Hosted free tier (HN launch): https://core.heysol.ai
We designed CORE for complex, evolving memory where text files break down.
Example: Health conversations across ChatGPT, Claude, etc. where your parameters change over time.
A text file can't give you: "What medications have I tried, why did I stop each one, and when?" or "Show me how my symptoms evolved over 6 months."
For timeline and relational memory, CORE wins. For static facts, text files are enough i guess.