RembrREMBR

What is REMBR?

A production-ready, MCP-native memory service designed for AI agents and assistants

The Problem: Context Amnesia

AI assistants and agents traditionally suffer from context amnesia- they forget everything between sessions. While chat history helps within a conversation, it doesn't scale:

Bloated Context Windows
Chat transcripts make context windows expensive and slow
No Semantic Search
Can't search across historical context intelligently
No Multi-Agent Knowledge Sharing
Different agents can't effectively share learnings
Difficult Debugging
Hard to trace causality and debug agent decisions

The Solution: REMBR Memory Layer

REMBR provides a persistent, searchable memory layer that AI systems can read from and write to via MCP tools, solving all the context amnesia problems:

Persistent Storage
Memories survive beyond conversations and sessions
Hybrid Search
Semantic + full-text search for relevant context retrieval
Multi-Agent Coordination
Shared knowledge base across agents and systems
Stored Decision Rationale
Debug decisions with temporal queries and rationale stored in memory metadata

Key Features

MCP-Native Architecture

Built from the ground up for the Model Context Protocol, not just an API with an MCP wrapper. This means:

  • •Direct integration with Claude Desktop, VSCode, and all MCP clients
  • •Zero latency overhead from protocol translation
  • •OAuth 2.0 with dynamic discovery for seamless auth

Enterprise Multi-Tenancy

Layered tenant, project and user authorisation protects data, with FORCE RLS on memories and selected MCP tables:

  • •Service queries carry an explicit tenant scope and apply principal-aware predicates
  • •Optional project-level isolation for workspace segmentation
  • •SOC2-ready audit logging for compliance

Hybrid Search Engine

Best-of-both-worlds search combining semantic understanding with keyword precision:

  • •Vector similarity search with 768-dimensional embeddings
  • •Full-text search for exact phrase and fuzzy matching
  • •Weighted fusion (0.7 semantic + 0.3 text) for optimal results

RLM-Optimized

Built for Recursive Language Models - the paradigm of 2026:

  • •Immutable bounded snapshots for sub-agent handoffs
  • •Scoped relationship queries over authorised memories
  • •Temporal queries: "What did the agent know at time T?"

Common Use Cases

💬 Personal AI Assistants
Claude Desktop or VSCode assistants that remember your preferences, coding style, and project context across sessions.
🤖 Recursive Agent Systems
RLMs that decompose tasks into sub-agents, each with isolated context workspaces and shared knowledge.
📊 Knowledge Management
Team memory for storing decisions, patterns, and learnings that persist across projects and team members.
Ready to Get Started?
Follow the Quick Start guide to set up REMBR in under 10 minutes