Adaptive Memory Graph MCP server | MCP Fair

Adaptive Memory Graph MCP server | MCP Fair

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Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx adaptive-memory-graph==1.1.1

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1claude mcp add io.github.raskolnikovdd-adaptive-memory-graph -- uvx adaptive-memory-graph==1.1.1

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.raskolnikovdd-adaptive-memory-graph": {4      "command": "uvx",5      "args": [6        "adaptive-memory-graph==1.1.1"7      ]8    }9  }10}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Server facts

Local

Transport: stdio
Version: 1.1.1
Last checked

Tools

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

Add to Cursor Add to VS Code Run on PlugMCP

1{2  "mcpServers": {3    "io.github.raskolnikovdd-adaptive-memory-graph": {4      "command": "uvx",5      "args": [6        "adaptive-memory-graph==1.1.1"7      ]8    }9  }10}

.cursor/mcp.jsonMCP server

Transport: stdio
Status: Local

From the publisher

Documentation

Adaptive Memory Graph

An MCP server plugin that gives Claude persistent, intelligent memory across sessions. It stores knowledge as weighted, interconnected nodes in a graph that evolves through conversation — nodes that get used gain weight, unused ones decay and eventually archive.

Works with Claude Code and Claude Desktop.

Features

Weighted memory nodes — Important memories stay prominent; stale ones fade
Cross-domain connections — Link related knowledge across topics
Time-based decay — Graph self-prunes so only relevant memories persist
Encrypted storage — AES-256-GCM encryption with macOS Keychain key storage
Session logging — Tracks which memories were accessed and how they were received
Domain organization — Nodes organized by domain (e.g. health_and_safety, personal, ideas_and_projects)
Chat history ingestion — Review and extract knowledge from past Claude Code sessions

Installation

pip install adaptive-memory-graph

Or with uv:

uv pip install adaptive-memory-graph

Setup

Claude Code

claude mcp add adaptive-memory-graph -s user -- amg-server

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "adaptive-memory-graph": {
      "command": "amg-server"
    }
  }
}

Config file location:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Tools

Tool	Description
`amg_load_index`	Load lightweight graph index at session start
`amg_expand_branch`	Fetch full node content when contextually relevant
`amg_get_connected_nodes`	Find related nodes across domains
`amg_log_session`	Log session summary at conversation end
`amg_update_graph`	Process pending logs and apply weight decay
`amg_export_report`	Generate human-readable graph summary
`amg_manual_adjust`	Boost, decay, archive, or delete nodes
`amg_add_node`	Add new nodes to the graph
`amg_search_nodes`	Search nodes by title, summary, tags, or content
`amg_list_chat_sessions`	List available Claude Code chat sessions for review
`amg_read_chat_session`	Read a chat session's conversation content

How It Works

Session start — Claude calls amg_load_index to get a lightweight summary of your memory graph
During conversation — If a topic is relevant, Claude expands specific nodes for deeper context
Session end — Claude silently logs which nodes were accessed and suggests new ones
Between sessions — Weight decay runs, archiving memories that haven't been useful

Nodes are stored as encrypted JSON on disk (~/.amg/graph.json.enc). The encryption key is stored in your macOS Keychain.

Requirements

Python 3.10+
macOS (for Keychain-based encryption key storage)

License

MIT