What is REMBR?
A production-ready, MCP-native memory service designed for AI agents and assistants
TL;DR
REMBR is a memory-as-a-service platform that provides persistent, searchable context storage for AI applications through the Model Context Protocol (MCP).
MCP-native (not just an API wrapper)
Multi-tenant SaaS with RLS
Hybrid semantic + text search
RLM-optimized for recursive agents
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
Causal Tracing
Debug decisions with temporal queries and causality tracking
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
Production-grade Row-Level Security (RLS) ensures complete data isolation:
- •Every query automatically filtered by tenant_id at database level
- •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 Learning Machines - the paradigm of 2026:
- •Immutable snapshots for sub-agent handoffs and rollback
- •Causal tracing: "Why did the agent make this decision?"
- •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.