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Knowledge graph management system with BM25 + fuzzy search, inspired by filesystem concepts

Package Exports

  • @qty/memfs
  • @qty/memfs/index.js

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Readme

๐Ÿง  MemFS

A knowledge graph management system based on MCP server-memory, deeply refactored with filesystem-inspired design

๐Ÿ’ก Acknowledgments: Original @modelcontextprotocol/server-memory
Inspired by it, though heavily reimagined.

Node.js 22+


๐ŸŽฏ One-Line Description

Bringing modern filesystem concepts to knowledge graph management, combined with BM25 + fuzzy search for intelligent retrieval, designed for LLM-assisted humanities and social sciences research.

๐Ÿ“– ไธญๆ–‡็‰ˆๆ–‡ๆกฃ: docs/README_zh-CN.md


๐Ÿš€ Quick Start

Prerequisites

# Check Node.js version
node --version  # Must be v22.0.0 or higher

Installation & Run

Quickest way (npx):

npx @qty/memfs

Or clone and run:

# 1. Clone or download the project
cd MemFS

# 2. Install dependencies
npm install

# 3. Run server
node index.js

# Or specify custom storage path
MEMORY_DIR=~/my-knowledge
MEMORY_FILE_PATH=./custom/memory.jsonl

Configure as MCP Server

OpenCode format:

{
  "mcpServers": {
    "memory": {
      "type": "local",
      "command": ["npx", "-y", "@qty/memfs"],
      "enabled": true
    }
  }
}

VSCode / ClaudeCode / Cherry Studio / AstrBot format:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@qty/memfs"],
      "enabled": true
    }
  }
}

๐Ÿ“– Core Concepts

Concept Description Analogy
Entity Nodes in the knowledge graph File
Observation Properties/descriptions of entities inode
Relation Connections between entities Soft link
Reference Pointers from entities to observations Hard link

๐Ÿ’ก Core Design Philosophy

1. Transformer-Ready: On-Demand Retrieval

flowchart TD
    LLM["LLM"]
    ATT["Attention Mechanism"]
    MCP["MCP Protocol"]
    MEM["MemFS\nOn-demand structured data"]

    LLM --> ATT --> MCP --> MEM
    MEM -.-> |Returns results| LLM

Core principle: Don't stuff all knowledge into contextโ€”retrieve on demand.

2. Lightweight Design

Dimension Traditional Solution MemFS
Deployment Database + Vector Engine Pure Node.js
Resources GPU recommended, high memory CPU only
Explainability Black-box models BM25 transparent & controllable

3. Local JSONL Storage

{"type":"entity","name":"Weber","entityType":"person","definition":"German sociologist","observationIds":[1,2]}
{"type":"observation","id":1,"content":"Author of 'The Protestant Ethic'","createdAt":{"utc":"2026-02-08T13:53:07Z","timezone":"Asia/Shanghai"}}
{"type":"relation","from":"Weber","to":"Durkheim","relationType":"contemporary"}

Advantages: Editable with any text editor, Git-version-controllable, printable.

4. Humanities & Social Sciences Customization

Requirement Type Traditional MemFS
Knowledge units Functions/Classes Concepts/People/Documents
Relationship types Function calls Influence/Reference/Comparison
Update frequency High-frequency Low-frequency add, high-frequency reference

๐Ÿ“ฆ Complete API Tools (16 total)

Create

Tool Function Example
createEntity Batch create entities (with observations) Add concepts, people, documents
createRelation Create relations between entities Mark references, comparisons, influences
addObservation Add observations to existing entities Supplement reading notes

Read

Tool Function Example
searchNode BM25 + Fuzzy hybrid search Intelligent knowledge search
readNode Read complete entity information Get detailed attributes and relations
readObservation Batch read observations by ID Verify specific observations
listNode List all entity overviews Browse knowledge structure
listGraph Read entire knowledge graph Batch export, migration
howWork Get recommended workflow guidance Learn how to use the system

Update

Tool Function Example
updateNode Update entities and observations (Copy-on-Write) Modify definitions, update notes
updateObservation Batch update observation content Batch correct information

Delete

Tool Function Example
deleteEntity Delete entities and relations Remove outdated entries
deleteRelation Delete specific relations Unlink entities
deleteObservation Unlink observations (preserve observation) Remove references
getOrphanObservation Find orphan observations Discover invalid data
recycleObservation Permanently delete observations Clean up unused data

๐Ÿ” Hybrid Search (searchNode)

Core Features

Feature Description
BM25 Considers term frequency and document frequency
Fuzzy Search Tolerates typos, supports approximate matching
Query Tokenization Tokenize โ†’ Search individually โ†’ Aggregate โ†’ Deduplicate
Weighted Fusion BM25 0.7 + Fuzzy 0.3, combined ranking

Parameters

// Default hybrid search
await searchNode("functionalism");  // BM25 + Fuzzy

// Traditional keyword search
await searchNode("functionalism", { basicFetch: true });

// Custom parameters
await searchNode("sociology", {
    limit: 15,          // Return count
    bm25Weight: 0.7,    // BM25 weight
    fuzzyWeight: 0.3,   // Fuzzy search weight
    minScore: 0.01      // Minimum relevance threshold
});

Field Weights

Field Weight Description
name 5.0 Highest - entity name
entityType 4.0 Entity type
definition 4.0 Definition description
observation 3.0 Observation content

๐Ÿ”ง Filesystem-Inspired Design

Architecture Analogy

Filesystem Concept MemFS Implementation Solves
Inode Table Centralized observation storage Data redundancy
Hard Links Multiple entities reference same observation Shared reuse
Soft Links Entity relations Flexible associations
Copy-on-Write Copy-on-Write updates Concurrency safety
Orphan Detection Orphan observation cleanup Resource recovery

Observation Sharing

// Create two entities sharing the same observation
await createEntity([
  { name: "Zhang San", observations: ["Programmer"] },
  { name: "Li Si", observations: ["Programmer"] }
]);

// Under the hood: same observation ID is reused
{
  entities: [
    { name: "Zhang San", observationIds: [1] },
    { name: "Li Si", observationIds: [1] }
  ],
  observations: [
    { id: 1, content: "Programmer" }
  ]
}

Copy-on-Write

// Update a shared observation
await updateNode({
  entityName: "Zhang San",
  observationUpdates: [
    { oldContent: "Programmer", newContent: "Senior Programmer" }
  ]
});

// Result: Zhang San gets new observation, Li Si keeps original
{
  observations: [
    { id: 1, content: "Programmer" },      // Li Si uses
    { id: 2, content: "Senior Programmer" } // Zhang San's new observation
  ]
}

๐Ÿ“ Data Format

JSONL Storage

{"type":"entity","name":"Weber","entityType":"person","definition":"German sociologist","definitionSource":"Wikipedia","observationIds":[1,2]}
{"type":"observation","id":1,"content":"Author of 'The Protestant Ethic'","createdAt":{"utc":"2026-02-08T13:53:07Z","timezone":"Asia/Shanghai"}}
{"type":"observation","id":2,"content":"Contemporary with Durkheim and Marx","createdAt":{"utc":"2026-02-08T14:00:00Z","timezone":"Asia/Shanghai"}}
{"type":"relation","from":"Weber","to":"Durkheim","relationType":"contemporary"}

Storage Locations

Method Path
Default ~/.memory/memory.jsonl
Custom directory MEMORY_DIR=/path/to/data
Custom path MEMORY_FILE_PATH=/path/to/file.jsonl

๐Ÿงช Testing

# Full test suite (45 tests)
node test_full.mjs

# Hybrid search tests (38 tests)
node test_hybrid_search.mjs

# Observation search tests
node test_observation_search.mjs

โš™๏ธ Comparison with Original MCP Memory

Dimension Original MemFS
Observation Storage Embedded in entities Centralized + ID reference
Data Sharing Not supported Hard-link style sharing
Update Mechanism Direct overwrite Copy-on-Write
Search Capability Simple keyword BM25 + Fuzzy
Orphan Detection ็†่ฎบไธŠไธๅญ˜ๅœจๅญคๅ„ฟ่ง‚ๅฏŸ Supported
Cache Mechanism None 30s TTL
Windows Compatibility Unknown Graceful degradation

๐Ÿ“š Design Philosophy

What? You're still reading? Well, alright.

Honestly, this project started because:

  1. LLM context is limited โ€” can't stuff all knowledge into prompts
  2. Filesystem is a great invention โ€” handling "multiple data sharing same content" is mature
  3. Humanities research has special needs โ€” concepts, literature, citation relationships
  4. Controllability > SOTA โ€” no need for black-box vector models

So:

  • Borrow filesystem wisdom: inode table, hard links, copy-on-write
  • Search uses BM25 + Fuzzy: lightweight, explainable, transparent, controllable
  • Expose as tools: 16 MCP tools, LLM calls on demand

Result? โ€” A quiet, efficient, unobtrusive knowledge management tool.


๐Ÿ“„ License

Apache License 2.0


Manage knowledge the filesystem wayโ€”bringing order to chaos.