Adaptive Memory Graph

io.github.raskolnikovddPublished by GitHub account raskolnikovddv1.1.1Updated Oct 24, 2026

Persistent memory for Claude via weighted, interconnected knowledge nodes.

The publisher's own server: Memory.fit

VerifiedSTDIODesktop onlyFiles & StorageKnowledge & Memory

Overview

AI-generated overview

A local MCP server that gives Claude a persistent, weighted memory graph across sessions, with encrypted on-disk storage and time-based decay.

What you can do
Load a lightweight graph index at session start and expand individual nodes for deeper context when a topic is relevant, plus find related nodes across domains and search nodes by title, summary, tags or content. Add new nodes and manually boost, decay, archive or delete them. Log session summaries, process pending logs with weight decay, and export a human-readable graph report. List and read past Claude Code chat sessions to extract knowledge from them.
When to use it
Useful when you want Claude to remember facts, preferences and project context across separate conversations, and to let stale memories fade instead of accumulating. Best suited to macOS users of Claude Code or Claude Desktop who want memory stored locally rather than in a hosted service.
Requirements
Runs as a local process installed from PyPI (pip or uv) and started with the amg-server command; Python 3.10 or newer. macOS is required because the encryption key is kept in the macOS Keychain. No API keys or environment variables are declared.
Before you install
The server reads past Claude Code chat sessions, so conversation content can be ingested into the memory graph. It writes, archives and deletes memory nodes, and manual adjust can delete nodes outright. Memory is stored encrypted at a file under the user's home directory, with the key held in the macOS Keychain; anyone with access to that account can decrypt it.

Installation

In SourceWeft

  1. Open Adaptive Memory Graph in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

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

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

Source: README.md at commit 9f5b0ad

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.1.1LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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