The Memory DBMS for Agentic AI Applications and Institutional Knowledge

MaluDB is a the first database management system for both humans and AI Agents that stores memories and historical knowledge along with data. It's not just "what do we have?", but also "how did we get here?".

MaluDB not only stores your workflows and knowledge my also transforms them in SKILLS than can be used by AI Agents to solve tasks while saving tokens.

Built on PostgreSQL — the world's most trusted database.

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MaluDB reference architecture

Architecture

Architecture

MaluDB is the first enterprise level database management system for memories, knowledge, workflows, and skills for both humans and AI Agents. MaluDB is a sophisticated set of extensions built on top of PostgreSQL, the most trusted enterprise grade database. For more information, read the whitepapers: “A Scalable Database Management System for Long-Term Institutional Memory, Human-AI Knowledge Sharing, and Contextual Recall” and “MC2DB: Database-Native Governance for Enterprise MCP Tools”.

MaluDB Architecture

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Shared Memory for AI-Agent Teams
Memories, Knowledge, Facts
MC2DB - Databse Driven MCP
Built-in LLM Control
Skills Objects
Knowledge Ingestion
Memory Pipeline

Our Core Premises

Four Beliefs That Shape MaluDB

1
Memories, Not Just Facts

A database must store memories — context, provenance, and meaning — not just isolated facts.

2
One Memory System

Developers should not have to stitch multiple databases together to get a working memory system.

3
Real-Time Shared Memory

Agents need to share memory in real time — collaborating on the same fresh, evolving context.

4
Database-Governed Access

Access to LLMs and MCP servers should be governed by database security — not bolted on after.

Memory Object Lifecycle: Source → Claim → Fact → Memory → Workflow → Skill

About MaluDB

Organizations store data. They keep losing the story behind it.

Records, tickets, messages, logs, dashboards, and documents show what happened. The reasons, assumptions, evidence, and lessons stay scattered across tools — and rebuilt by hand every time a team changes or a system migrates.

MaluDB proposes a unified Memory DBMS that treats this as a database concern. Memory Objects are governed by the system: identity, provenance, validity, permissions, lifecycle, and audit live in the catalog — not in application code. Storage engines, embedding models, graph indexes, and LLMs remain replaceable mechanisms attached through controlled interfaces.

Read the White Paper

What MaluDB Does

Enterprise Database for AI Agent/Human Teams

MaluDB brings LLM access and MCP Server security into the enterprise database where it belongs. With MaluDB, developers and system administrators have complete control over prompts executed, MCP tools, data used by AI at the database level and down to row level security (RLS).

Historical Knowledge Ingestion

MaluDB ingests from the systems your organization already runs — emails, chat transcripts, tickets, meeting notes, documents, source control, operational logs, time-series, and relational databases — and preserves each artifact as a verbatim Source Package. Decades of scattered history become an addressable, reprocessable base for memory.

Real-Time Context Sharing

Active Memory Pools give humans, AI agents, and tools a shared, task-scoped working space. Collaborators load authorized memories, publish observations, mark pending Claims, and promote validated knowledge — so every agent in a workflow operates on the same fresh, permission-filtered context instead of drifting on stale copies.

Single Platform for Memory Search

One DBMS, four search modes working together: Relational for structured records, Compartmentalized Vector for semantic recall with permissions enforced inside candidate generation, Graph for traversing Relationship Edges, and Temporal for bitemporal validity over time. No more stitching three products together to answer one question.