Discover why standard MCP wiring makes context persistence optional (the model calls the tool only when it decides to) and what to do instead. We walk through how to treat persistence as control flow rather than reasoning, using your own code as the MCP client, so the write always happens. Finally, we prove this with a working Strands agent that logs every step to Meko, and audit the trace from a separate process to confirm the context survived.
Developers and engineering teams building production multi-agent AI applications need their agent systems to learn and improve collectively, not just store and retrieve data. This blog explores Meko’s agent memory features and showcases a short video by Yugabyte Developer Advocate Heather Downing that demonstrates exactly how Meko gives AI agents persistent, shareable memory.
With Meko, your project context lives in a datapack any MCP-connected client can read. This allows you to switch tools without losing context, share useful information with your team while keeping selected data private, and capture not just what decision was reached, but why. All this is built on a PostgreSQL-compatible distributed database that scales as you grow. This blog details what each Meko component does, why it’s important, and the steps you need to take to try it for yourself!
YugabyteDB is officially supported in Google’s MCP Toolbox for Databases, making it easier for you to build production AI agents with confidence. Discover how developers can access YugabyteDB’s distributed SQL capabilities, including load balancing, topology-aware routing, and data sovereignty controls, as documented and tested configuration options rather than custom workarounds.
