“Where Were We?” Watch Meko Give AI Agents a Shared Context

Meko is an innovative agent-native data infrastructure built on YugabyteDB that enhances the efficiency of multi-agent AI systems by providing them with a shared, persistent memory. This shared memory allows agents to learn collectively, reducing the redundancy and token costs often associated with inter-agent misalignment, where agents unnecessarily re-fetch and re-explain data. Meko consolidates various data types—vector, relational, graph, and search—within a unified Postgres-compatible database, accessible through a single MCP endpoint, which facilitates seamless integration with multiple agent frameworks like Claude, Cursor, and Codex. In a demonstration by Yugabyte Developer Advocate Heather Downing, Meko showcases its capability to enable agents to recall past decisions and tasks effortlessly, with decision tracing and auditability ensuring transparency in how knowledge is acquired and shared. This approach not only optimizes token usage but also simplifies infrastructure needs by replacing disparate data systems with a unified, serverless architecture, ultimately making Meko a cost-effective solution for scalable AI development.

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