広告掲載
MCP.so
ログイン

Memory MCP Server

@jcdiv47

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

ライセンス

MIT

トランスポート

stdio

作者

jcdiv47

Memory MCP Server について

概要はまだありません

#mcp-server-memory#knowledge-graph#ai-memory

Memory MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-server-memory": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

Memory MCP Server のツール

9

Create multiple new entities in the knowledge graph

Create multiple new relations between entities

Add new observations to existing entities

Delete entities and their associated relations

Delete specific observations from entities

Delete specific relations from the graph

Read the entire knowledge graph

Search for nodes matching a query

Retrieve specific nodes by name

概要

What is Memory MCP Server?

A Python implementation of an MCP server that processes and saves memory in the form of a knowledge graph. It enables AI systems to store, retrieve, and query structured data (entities, relations, observations) across sessions.

How to use Memory MCP Server?

Install via uv add mcp-server-memory or pip install mcp-server-memory. Run the server with the mcp-server-memory command. Optionally set the MEMORY_FILE_PATH environment variable to customize the memory file location. Configure for MCP clients like Claude or Cursor by adding the server definition to their settings files.

Key features of Memory MCP Server

  • Create and manage entities with structured information
  • Establish typed relations between entities
  • Add observations to existing entities
  • Delete entities, relations, and observations
  • Search nodes in the knowledge graph
  • Retrieve specific nodes by name

Use cases of Memory MCP Server

  • Persist AI assistant memory across conversations
  • Build and query a structured knowledge base from chat sessions
  • Enable context recall by storing named entities and their relationships
  • Facilitate long‑term agent memory in multi‑turn interactions

よくある質問

1

How do I install and run the server?

Install using `uv add mcp-server-memory` or `pip install mcp-server-memory`, then run `mcp-server-memory`. Optionally set the `MEMORY_FILE_PATH` environment variable for a custom storage location.

2

Where is the knowledge graph data stored?

By default, data is saved in a `memory.json` file in the package directory. You can change the path with the `MEMORY_FILE_PATH` environment variable.

3

What MCP tools does the server expose?

It exposes nine tools: `create_entities`, `create_relations`, `add_observations`, `delete_entities`, `delete_observations`, `delete_relations`, `read_graph`, `search_nodes`, and `open_nodes`.

4

What are the runtime dependencies?

The server requires Python and either `uv` or `pip` for installation. It uses the MCP (Model Control Protocol) interface.

5

Are there configuration examples for popular MCP clients?

Yes, the README provides example JSON configurations for both Claude (`claude_desktop_config.json`) and Cursor (`mcp.json`), using either `uv` or a pip‑installed Python module.

コメント

「メモリとナレッジ」の他のコンテンツ

M1K3

Round-Tower

Local AI agent for your Mac with a built-in MCP server: 18 tools for your notes and PDFs, a temporal memory graph, an on-device LLM and a neural voice. No account, no telemetry. No data centers required.

7

The Cloud

Teka

The Cloud is the AI-native workspace that gives every AI — Claude, ChatGPT, Gemini, and other Frontier & Free or Local models — shared memory and real agents. Not cloud computing: thecloud.so is one workspace where your

Memlord

MyrikLD

Memlord is a remote MCP server available at https://app.memlord.com.

server-noonien - shared memory MCP

sequico

Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.

Codebase Memory

DeusData

High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 162 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

46.7K
広告掲載