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
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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
Enable intelligent memory search with Natural Memory Triggers.
Facilitate team collaboration through OAuth 2.1.
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 a new memory or update existing memory in the hybrid backend system.
Search memories using intelligent semantic search with natural language queries.
Retrieve specific memories by ID or context from local or cloud storage.
Delete a memory from the hybrid storage system.
List all memories with optional filtering and pagination.
Trigger memory consolidation with decay scoring inspired by dream processes.
Get natural memory triggers with relevance scores for current context.
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
Discussion
Questions & comments · 0
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