io.github.raskolnikovdd/adaptive-memory-graph

Official

by raskolnikovdd · Python

Adaptive Memory Graph

Persistent memory for Claude via weighted, interconnected knowledge nodes.

Stores persistent weighted knowledge nodes for Claude to build interconnected memory.

README.md3 KBView on GitHub

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

bash

pip install adaptive-memory-graph

Or with uv:

bash

uv pip install adaptive-memory-graph

Setup

Claude Code

bash

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

Claude Desktop

Add to your claude_desktop_config.json:

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

Install

claude_desktop_config.json

{
  "mcpServers": {
    "adaptive-memory-graph": {
      "command": "uvx",
      "args": [
        "adaptive-memory-graph"
      ]
    }
  }
}