
Memoer (Persistent Memory Storage)
Provides persistent memory storage for AI assistants using SQLite, allowing them to create, retrieve, update and delete memories with filtering by app, category, and other criteria.
Provides persistent memory storage for assistants to create and retrieve memories with filtering options by app name, category, and result limits through a SQLite database with automatic setup.
What it does
- Store persistent memories in SQLite database
- Retrieve memories with filtering options
- Update and delete existing memories
- Organize memories by user, app, and category
- Search memories semantically with Qdrant integration
Best for
About Memoer (Persistent Memory Storage)
Memoer (Persistent Memory Storage) is a community-built MCP server published by geli2001 that provides AI assistants with tools and capabilities via the Model Context Protocol. Memoer offers unlimited free cloud storage and data management with persistent memory via SQLite for assistants—secure, It is categorized under ai ml.
How to install
You can install Memoer (Persistent Memory Storage) in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
Memoer (Persistent Memory Storage) is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
@memoer-mcp
A minimal, pluggable memory management library for Node.js, inspired by OpenMemory. Supports local SQLite storage, semantic search (Qdrant), and OpenAI embeddings. No web server, no frontend—just a programmatic API.
Features
- Add, list, update, delete memories
- Semantic search (Qdrant)
- User, app, and category support
- Local SQLite (via Prisma)
- TypeScript-first
Getting Started
-
Install dependencies:
npm install -
Initialize the database:
npx prisma migrate dev --name init -
Configure the MCP: Create or update your MCP configuration file (e.g.,
mcp_config.json) as follows:{ "memoer-mcp": { "command": "npx", "args": ["memoer-mcp@latest"], "env": { "DATABASE_URL": "file:/Users/{your_username}/{any_folder_path}/memoer.db" //macOS example } } } -
Use the library in your project:
import { MemoerMCP } from "@memoer-mcp"; const memoer = new MemoerMCP(); // Example: Adding a memory await memoer.addMemory({ title: "My First Memory", content: "This is the content of my first memory.", category: "Personal" }); // Example: Listing memories const memories = await memoer.listMemories(); console.log(memories);
Development
- Edit the Prisma schema in
prisma/schema.prisma - Run
npx prisma generateafter changes - Source code in
src/
This library is now fully functional and ready for use in your projects!
### MCP Configuration Example
In your `mcp_config.json`, you can configure the `memoer-mcp` command as follows:
```json
{
"memoer-mcp": {
"command": "npx",
"args": ["memoer-mcp@latest"],
"env": {
"DATABASE_URL": "file:/Users/{your_username}/{any_folder_path}/memoer.db" //macOS example
}
}
}
Explanation of Configuration
- command: This specifies the command to run, which in this case is
npxto execute thememoer-mcppackage. - args: This is an array of arguments passed to the command. Here, it specifies the package to run.
- env: This section allows you to set environment variables needed for your application. The
DATABASE_URLpoints to your SQLite database file.
Usage
With this setup, you can now run memoer-mcp from your command line or integrate it into your application as shown in the examples. This configuration allows you to manage memories effectively using the memoer-mcp library.
If you have any further questions or need additional examples, feel free to ask!
License
This project is licensed under the MIT License - see the LICENSE file for details.
Alternatives
Related Skills
Browse all skills"Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants."
Run a local Agent Memory Service for persistent self-improvement with proper Ed25519 cryptography. Fixed signature implementation for reliable memory storage and retrieval.
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm
Master Kubernetes storage management and networking architecture. Learn persistent storage, network policies, service discovery, and ingress routing.
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).