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Adaptive Memory Graph

by raskolnikovddversion 1.1.1i★ –iRuns on your computer
Registry nameio.github.raskolnikovdd/adaptive-memory-graph

Install

Overview

Description from the maker (original English)

Persistent memory for Claude via weighted, interconnected knowledge nodes.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

claude mcp add adaptive-memory-graph -- uvx adaptive-memory-graph

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "adaptive-memory-graph": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "adaptive-memory-graph"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account raskolnikovdd, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7251 servers in this category
  • Published to the registry more recently than 5% of the other 7251 servers in this category
  • Basic checks equal to or better than 100% of the other 7251 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Adaptive Memory Graph safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.1.1) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

ToolDescription
amg_load_indexLoad lightweight graph index at session start
amg_expand_branchFetch full node content when contextually relevant
amg_get_connected_nodesFind related nodes across domains
amg_log_sessionLog session summary at conversation end
amg_update_graphProcess pending logs and apply weight decay
amg_export_reportGenerate human-readable graph summary
amg_manual_adjustBoost, decay, archive, or delete nodes
amg_add_nodeAdd new nodes to the graph
amg_search_nodesSearch nodes by title, summary, tags, or content
amg_list_chat_sessionsList available Claude Code chat sessions for review
amg_read_chat_sessionRead a chat session's conversation content

How It Works

  1. Session start — Claude calls amg_load_index to get a lightweight summary of your memory graph
  2. During conversation — If a topic is relevant, Claude expands specific nodes for deeper context
  3. Session end — Claude silently logs which nodes were accessed and suggests new ones
  4. 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

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