广告合作
MCP.so
登录

SelfMemory MCP Server

@SelfMemory

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

SelfMemory

提交者

Shrijayan Rajendran

关于 SelfMemory MCP Server

It is a open-source universal memory engine where users can store and retrieve their AI conversations and context across different models. Users can add memories through MCP, SDK, or a website selfmemory.com Over time, this will evolve into a one-stop memory hub with note-taking

#memory#rag#agent#long-term-memory

SelfMemory MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "SelfMemory": {
      "url": "https://mcp.selfmemory.com/mcp/"
    }
  }
}

SelfMemory MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,直接连接服务器端点实时获取工具列表。

概览

What is SelfMemory?

SelfMemory is an open-source universal memory engine that lets users store and retrieve AI conversations and context across different models. It offers multiple access methods—MCP, an SDK, and the website selfmemory.com—and over time it will evolve into a one-stop memory hub with note-taking and chatbot features. For businesses, SelfMemory serves as a knowledge backbone that stores project context, organizational knowledge, documents, and data sources to power company-wide AI systems.

How to use SelfMemory?

Users can add memories through the MCP protocol, an SDK, or directly via the website selfmemory.com. No specific installation or configuration commands are provided in the README.

Key features of SelfMemory

  • Open-source universal memory engine
  • Store and retrieve AI conversations across models
  • Multiple access methods: MCP, SDK, website
  • Planned evolution into a memory hub with note-taking and chatbot
  • B2B knowledge backbone for organizational data

Use cases of SelfMemory

  • Storing and retrieving personal AI conversations across different models
  • Maintaining context continuity in multi-model chat workflows
  • Building a centralized organizational knowledge base for company-wide AI systems

常见问题

1

Is SelfMemory open-source?

Yes, SelfMemory is an open-source universal memory engine.

2

How can I add memories to SelfMemory?

You can add memories via the MCP protocol, an SDK, or the website selfmemory.com.

3

What is the future direction of SelfMemory?

It will evolve into a one-stop memory hub featuring note-taking and chatbot capabilities.

4

Can SelfMemory be used for business applications?

Yes, for B2B it becomes a knowledge backbone that stores project context, organizational knowledge, documents, and data sources to power company-wide AI systems.

5

Does SelfMemory work with any specific AI model?

The README indicates it works across different models, making it model-agnostic rather than tied to a single provider.

评论

记忆与知识 分类下的更多 MCP 服务器

ctxfile

ctxfile

Local-first MCP server that snapshots your project's working state into one context object. Open-core, privacy-first.

2

Sensefold

SenseFold Labs

Personal context for every AI: search, read, and write back to your private Markdown library of articles, threads, PDFs, notes, and captured ChatGPT/Claude/Gemini/Grok conversations. Remote server, OAuth 2.1 paste-and-au

Azquo

Azquo Holdings Ltd

Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,

Orcha

westonhancock

Orcha is a unified context layer for AI tools and agents. It stores and indexes and exposes organizational, team, or individual knowledge. Files, structured databases, and connected sources live in one workspace, and age

Stele

Stele-Dev

Shared memory for AI coding agents. Claude Code, Cursor and Codex read your project's decisions, lessons and tasks before they act.

广告合作