Meko

AI agents generate and consume dynamic data such as conversation histories, contextual knowledge, operational traces, and long-term memory and today that data is scattered across relational databases, vector stores, document stores, and caches that IT teams must wire together themselves. Meko replaces that stack with a single, purpose-built data layer for agentic applications. It exposes agent-native actions like "add knowledge" and "promote memory" through standard MCP interfaces, while Meko automatically manages how data is stored, indexed, and optimized underneath. Every project lives in a Datapack: a portable, multi tenant store containing conversations, per-agent memories, uploaded knowledge files, and a traceable record of every action an agent took. Memories are private by default and shareable by choice, you promote the learnings worth keeping to Collective Memory, so when one agent picks up where another left off, it inherits not just the output but the reasoning that shaped it. Because Meko works over MCP, the same context follows you whether you're working in Claude, Cursor, Codex, or any other MCP-connected client. Meko is architected for the bursty, unpredictable economics of agentic workloads: costs stay near zero when agents are idle and scale seamlessly when they're active, with older data automatically tiered to object storage and warmed back up on demand. Every memory read and write routes through a single MCP endpoint backed by a unified, PostgreSQL-compatible distributed database (YugabyteDB), giving regulated enterprises a complete, auditable trail of what agents learned, how they shared it, and what operations produced it - turning compliance with regulations like the EU AI Act into a property of the architecture rather than an afterthought.