Meko: Solving the Multi-Agent Memory and Knowledge Problem

Building production-ready agentic AI applications often reveals a harsh reality: agents rarely fail because they lack reasoning capabilities, but because they cannot effectively share what they know. When multiple agents collaborate, the lack of a unified memory and knowledge layer leads to fragmented explainability, high operational costs, and a "start-from-scratch" mentality where every agent must rediscover the same lessons.

Traditionally, developers have attempted to solve this by stitching together a patchwork of databases—PostgreSQL for relational data, Pgvector for semantic search, graph databases for memory, and object stores for conversation logs. This approach creates data silos and introduces significant latency. Meko emerges as an agent-native data infrastructure designed to abstract this complexity, allowing developers to focus on building systems rather than managing infrastructure.

The Core Challenge: Inter-Agent Misalignment

According to the Multi-Agent System Failure Taxonomy (MAST), approximately 36.9% of failures in multi-agent systems stem from inter-agent misalignment. This occurs when agents operate in isolation, meaning one agent's learned behavior or knowledge updates do not propagate to the rest of the system.

When work is handed off from Agent A to Agent B, typically only the curated output is transferred. The underlying reasoning, assumptions, and intermediate decisions are lost. Meko solves this by preserving the full context of the decision-making process, ensuring that the inheriting agent understands not just what was decided, but why.

The Meko Architecture: The Datapack

At the heart of Meko is the datapack, which interacts with agentic frameworks through a single Model Context Protocol (MCP) endpoint. Instead of shoehorning agent data into generic tables, Meko utilizes four native data constructs:

1. Collective Memory

Unlike frameworks that give each agent isolated memory, Meko implements a compounding system. It supports five distinct memory types:

• Working Memory: Transient state for active tasks.
• Episodic Memory: Task histories and interaction logs.
• Semantic Memory: Durable facts and domain knowledge.
• Procedural Memory: Learned workflows and tool-use patterns.
• Shared Memory: Common ground for coordination across all agents in a datapack.

When an agent learns something new, that information is automatically promoted from private memory to the shared knowledge layer, benefiting every other agent in the system.

2. Unified Knowledge

Knowledge in AI systems is dynamic. Meko automatically processes diverse sources—PDFs, SQL tables, HTML, and live data feeds—generating embeddings and summaries without requiring manual pipeline management.

This enables "hybrid queries," where an agent can retrieve data that is semantically similar to a task, created within a specific timeframe, and tagged with a specific ID—all in a single PostgreSQL statement rather than three separate round-trips to different databases.

3. Decision Traces

For production systems, trust and auditability are non-negotiable. Meko captures decision traces, which are full chains of thought including the initial prompt, the agent's plan, tool calls, and the resulting knowledge updates. This is particularly critical for compliance with regulations like the EU AI Act, which may require long-term retention of decision-making records for high-risk AI systems.

4. Tiered Conversations

To balance performance and cost, Meko employs a three-tier storage model for conversation history:

• Hot Storage: Recent conversations stay in YugabyteDB for millisecond latency.
• Warm Storage: Older conversations auto-tier to S3 object storage.
• Cold Storage: Beyond a certain window, only summaries are kept, though full verbatim history is preserved for completeness.

Infrastructure and Integration

Meko is built on YugabyteDB, a horizontally scalable, PostgreSQL-compatible distributed database. This allows it to support SQL, NoSQL, vector, time-series, and graph queries within a single layer.

Because it adheres to the MCP standard, Meko integrates seamlessly with tools like Claude Code, Claude Desktop, and Cursor. Its serverless, multi-tenant architecture is specifically tuned for the bursty nature of agentic workloads, ensuring that idle agents consume near-zero resources while active agents scale instantly.

Summary of Production Patterns

Meko is designed to support six critical patterns for engineering teams:

1. Context-Preserved Handoffs: Reasoning is passed along with results.
2. Collective Learning: Lessons are persisted across workflow runs.
3. Auditable Trails: Full traceability for compliance and tuning.
4. Economic History: Automated tiering of conversation logs.
5. Agent Resumability: Full state persistence for pausing and resuming runs.
6. Portable Project Memory: Shared coding standards and norms across teams.