Memory MCP Server

@jcdiv47

Repository

Basic information

About Memory MCP Server

No overview available yet

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

Memory MCP Server config

Add this server to your MCP-compatible client using the configuration below.

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

Memory MCP Server tools

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

Overview

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

Frequently asked questions

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.

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.

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`.

What are the runtime dependencies?

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

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.

Comments

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