How Meko fits in your stack

Where Meko sits in your architecture and how it deploys

Meko is the agent-native data infrastructure that enables multi-agent systems to learn together, building collective memory and shared knowledge that compounds across the entire system.

It supports:

  • Collective memory. Learning compounds across the entire system, not just per-agent memory.
  • Shared knowledge. Meko builds knowledge over time from conversations, real-time data sources, and slower-changing knowledge bases.
  • Decision Traces. Connect raw conversations to execution traces of how LLMs think, agents process memory and knowledge, learn from it, and share that learning.

Meko is serverless, and multi-tenant (multi-agentic). It provides memory, conversation history, shareable knowledge, and full chain-of-thought traceability.

Architecture

Meko integrates with any agentic framework through a standard MCP interface. It is built on top of a unified distributed PostgreSQL data layer that supports vector, SQL, NoSQL, graph, and search.

Meko sits between your agent SDK and the underlying storage, providing a unified data layer that replaces multiple standalone systems.

Meko Architecture Diagram

What you get

A datapack is the isolation unit for agent data in Meko. Datapacks can be shared by many users and connected to many agents. A datapack incorporates the following:

Capability Powered by (internal) API
Conversation history PostgreSQL (YugabyteDB YSQL) conversation_* tools
Memory Graph memory + vector search (pgvector, hnswlib) memory_* tools
Knowledge base RAG pipeline (pg_dist_rag) + vector search knowledgebase_* tools
Decision traces LLM reasoning traces and execution logs Observability dashboard
Observability, security, RBAC Spans the full stack, not bolted on per-component —

Meko Datapack Internals Diagram

The underlying storage technologies are managed internally and not directly accessible.

What Meko replaces

Without Meko, building an agentic application typically requires wiring together:

  • A relational database for structured data
  • A vector database for embeddings and similarity search
  • A graph database for entity relationships
  • Per-database observability, security, and RBAC
  • Custom integration code for stitching together search results from each database

What Meko Replaces Diagram

Meko replaces all of this with a single system. The memory, knowledge, and conversation layers share state and operate as one.

Framework compatibility

Meko works with any agentic framework, including:

  • CrewAI
  • LangChain / LangGraph
  • OpenAI Agents SDK
  • Any MCP-compatible client

Deployment

  • Cloud: Currently, Meko is a cloud service, managed by YugabyteDB.