MCP

Synchronize Memory Across AI Tools

Production-ready MCP memory service with zero database locking, hybrid backend delivering 5ms local reads with cloud sync, and intelligent memory search for AI

Works with cloudflareoauthsqliteonnx

90
Spark score
out of 100
Status Verified
Updated 4 months ago
Version 1.0.0
Models

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Why it matters

Integrate a production-ready memory service for AI assistants, offering zero database locking and a hybrid backend for fast local reads with cloud synchronization.

Outcomes

What it gets done

01

Enable intelligent memory search with Natural Memory Triggers.

02

Facilitate team collaboration through OAuth 2.1.

03

Provide multi-client support for various AI applications.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-mcp-memory-service | bash

Capabilities

Tools your agent gets

store_memory

Store a new memory or update existing memory in the hybrid backend system.

search_memory

Search memories using intelligent semantic search with natural language queries.

retrieve_memory

Retrieve specific memories by ID or context from local or cloud storage.

delete_memory

Delete a memory from the hybrid storage system.

list_memories

List all memories with optional filtering and pagination.

trigger_memory_consolidation

Trigger memory consolidation with decay scoring inspired by dream processes.

get_memory_triggers

Get natural memory triggers with relevance scores for current context.

sync_memories

Manually trigger synchronization between local cache and cloud backend.

Overview

mcp-memory-service MCP Server

What it does

A production-ready MCP server that provides persistent memory capabilities for AI assistants with hybrid storage, intelligent search, and team collaboration features.

How it connects

Use this when you need AI assistants to retain context across sessions with fast local access, automatic memory retrieval, and optional cloud synchronization for team environments.

Source README

Production-ready MCP memory service with zero database locking, hybrid backend (fast local reads + cloud synchronization) and intelligent memory search for AI assistants. Includes auto-configuration for multi-client access, local reads in 5ms with background synchronization through Cloudflare, Natural Memory Triggers with 85%+ accuracy, and team collaboration via OAuth 2.1.

Installation

PyPI

pip install mcp-memory-service

UV

uv pip install mcp-memory-service

From Source

git clone https://github.com/doobidoo/mcp-memory-service.git
cd mcp-memory-service && python install.py

Docker

docker-compose up -d

Smithery

npx -y @smithery/cli install @doobidoo/mcp-memory-service --client claude

Configuration

Claude Desktop

{
  "mcpServers": {
    "memory": {
      "command": "memory",
      "args": ["server"],
      "env": {
        "MCP_MEMORY_STORAGE_BACKEND": "hybrid"
      }
    }
  }
}

Features

  • Zero database locking with concurrent access
  • Hybrid backend with fast local reads (5ms) and cloud synchronization
  • Natural Memory Triggers with 85%+ accuracy
  • Intelligent memory search and automatic context injection
  • Team collaboration via OAuth 2.1
  • Memory consolidation system with decay scoring inspired by dreams
  • Multi-client support (Claude Desktop, VS Code, Cursor, Continue, and 13+ AI applications)
  • SQLite-vec with ONNX embeddings for offline operation
  • Background synchronization through Cloudflare
  • Git-aware context integration

Environment Variables

Optional

  • MCP_MEMORY_STORAGE_BACKEND - Storage backend type (hybrid, cloudflare, sqlite)

Notes

Supports Python 3.12+ (note on Python 3.13 compatibility for sqlite-vec). macOS users may need Homebrew Python for SQLite extension support. Initial setup includes automatic model download (~25MB). The visible memory embedding feature displays top-3 memories at session start with relevance scores.

Trust

How it checks out

Official By maintainer
Downloads 0

Discussion

Questions & comments · 0

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