A Vectorized Memory Layer Gives Agents Shared Context
Important work context can be written once, stored in memory, and read back by agents through an MCP server later.
The Problem
Useful work context disappears too easily when it only lives inside chats, scattered notes, or someone's head.
What Was Built
A memory repository where information can be added, vectorized, and stored so agents can read from it and write back to it through an MCP server.
Where AI Sits in the Workflow
AI agents can retrieve and contribute useful context instead of starting fresh each time. A person can still decide which memories are worth promoting or correcting.
Tools Used
The Result
Important context becomes reusable across sessions and agents instead of getting recreated over and over.
Key Insight
AI gets much more useful when memory is treated like infrastructure.
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