Power your agent
URL ของผู้เผยแพร่เอง ไม่ใช่เอนด์พอยต์ของ PlugMCP
Host a copy with our keys and logs.
https://memofs.dev/api/v1/projects/{projectId}/mcpปุ่ม Claude ด้านบนจะเปิด Claude พร้อมกรอกชื่อและ URL ให้แล้ว คุณยังต้องยืนยันที่นั่นเอง
claude mcp add --transport http dev.memofs-mcp-server https://memofs.dev/api/v1/projects/{projectId}/mcpRun in a terminal.
{ "mcpServers": { "dev.memofs-mcp-server": { "command": "npx", "args": [ "-y", "mcp-remote", "https://memofs.dev/api/v1/projects/{projectId}/mcp" ] } }}claude_desktop_config.json
URL แบบรีโมตหรือคำสั่งบนเครื่องของคุณ? การติดตั้งสองแบบต่างกันอย่างไร
รันบน PlugMCP
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.
เปิดจากผู้เผยแพร่
Open-source, file-first memory runtime for AI agents.
File-first memory runtime for AI agents. Store, recall, and synchronize memory using plain files on disk — local-first by default, with optional cloud sync.
Most AI memory systems are database-first, vendor-locked, hard to inspect, and hard to version. MemoFS inverts that: your agent's memory lives as Markdown and JSONL under a directory you can , , and roll back.
Persistent memory and virtual filesystem MCP server for AI agents.
.memofs/catgit diff.memofs/
├── config.json # Workspace settings and engine routing
├── manifest.json # Asset registry tracking and hashes
├── memory/
│ ├── core.md # Durable, project-wide facts (Markdown)
│ └── notes.md # Timestamped notes and logs (Markdown)
├── events/
│ └── conversations.jsonl # Chronological interactions for recall
├── graph/
│ ├── nodes.jsonl # Entities extracted from memory
│ └── edges.jsonl # Relational connections
├── archive/ # Cold storage for deprecated memories
│ └── <id>.json # Full-fidelity archived memory records
└── snapshots/
└── snap_123.json # Versioned restore checkpoints
MemoFS serves two primary paths: users running AI agents day-to-day and engineers building custom agents & runtimes.
Initialize MemoFS in any project in under a minute. The CLI creates .memofs/, sets up project rules, pre-wires platform lifecycle hooks, and configures the local MCP server:
npx @memofs/cli init
Your agent now automatically inherits durable memory across sessions — zero manual prompting required.
Embed the runtime directly into your TypeScript or Node.js agent architecture:
npm install @memofs/core
import { MemoFS } from "@memofs/core";
import { createNodeFsMemoryStore } from "@memofs/core/node-fs";
// Initialize a Node.js filesystem-backed memory store
const store = createNodeFsMemoryStore({
rootDir: ".",
});
// Create the unified client
const memo = new MemoFS({
store,
projectId: "my-app",
mode: "local",
});
// Read project-wide core memory (core.md)
const core = await memo.core.read();
console.log(core);
// Record a durable note (notes.md)
await memo.notes.record({
content: "User prefers TypeScript with ESM modules.",
kind: "preference",
});
// Recall works offline (lexical BM25 + fuzzy matching) with zero config
const hits = await memo.recall("TypeScript configuration");
To upgrade to semantic vector search, plug in an embedder adapter like OpenAI (@memofs/adapter-openai) or Voyage AI (@memofs/adapter-voyage). For zero-API-key local vector search, enable the ONNX embedder (@memofs/adapter-transformers) to run embeddings completely in-process.
Your App / Agent / MCP client
│
▼
MemoFS (local-first runtime)
├─ .read() / .write() / .recall()
├─ .snapshot.create() / .restore()
├─ AgentFS (lease-locking & virtual paths)
└─ .sync * (Cloud sync pushes and pulls)
read() / write() / recall() — core client methods
│
▼
.memofs/ (plain files on disk)
├─ memory/core.md ├─ memory/notes.md
├─ events/*.jsonl ├─ graph/{nodes,edges}.jsonl
└─ snapshots/ manifest.json
│ git-friendly, inspectable, versionable
▼ (optional)
MemoFS Cloud
The runtime resolves configuration from constructor options → env vars → .memofs/config.json.
Three runtime modes are supported: local (filesystem-only, default), hybrid (local + cloud sync with read/write policies), and memory (in-memory volatile, ideal for tests).
memofs why <id>) backed by append-only action receipts with task correlation (taskRef).stream.jsonl) with typed coordination events (agent.heartbeat, resource.intent, task.status, agent.hint) filtered out of durable recall.memofs lint CI/CD rule pipeline detecting broken references, contradictory assertions, and broken provenance links.unverified status before semantic archiving.success / failure / aborted outcome gates governing durable memory promotion and cleanup.MemoFS is structured as a monorepo containing 16 published public packages under the @memofs/ scope. The CLI ships as @memofs/cli and installs the memofs command.
The core runtime is open source (MIT) and fully functional locally. You do not need a cloud account to run MemoFS.
MemoFS Cloud is the memory plane for your agents: it keeps every machine, teammate, and agent on the same memory, and gives you a dashboard to see and govern it.
memofs/
├── apps/
│ └── docs/ # React Router & Fumadocs documentation (docs.memofs.dev)
├── packages/ # 16 published @memofs/* packages
├── tooling/ # Private @repo/* workspace build packages
├── benchmarks/ # Workspace benchmarking suite
├── examples/ # Runnable examples
└── package.json
Run these command tasks from the repository root:
# Install all dependencies
pnpm install
# Build all packages and applications
pnpm build
# Run TypeScript compilation checks
pnpm typecheck
# Run unit tests across all packages
pnpm test
# Run code style and lint checks (Biome)
pnpm check
# Fix linting and formatting issues automatically
pnpm format-and-lint:fix
# Run local documentation dev server
pnpm docs:dev
# Build documentation locally
pnpm docs:build
See CONTRIBUTING.md for details on formatting, testing, and pull requests.
For roadmap targets, see ROADMAP.md.
For security reports, refer to SECURITY.md — do not open public issues for security vulnerabilities.
MIT. See LICENSE.
Persistent memory and virtual filesystem MCP server for AI agents.
{ "mcpServers": { "dev.memofs-mcp-server": { "url": "https://memofs.dev/api/v1/projects/{projectId}/mcp", "headers": {} } }}เครื่องมือ
ยังไม่มีรายการเครื่องมือ เซิร์ฟเวอร์แบบรีโมตจะได้รายการหลังการตรวจสอบสถานะครั้งแรก
FAQ
คัดลอกคอนฟิก http สำหรับ Cursor, Claude Desktop, Claude Code หรือ VS Code ได้ในหน้านี้ คอนฟิกนี้ใช้การติดตั้งของผู้เผยแพร่ ไม่ใช่พร็อกซีของ PlugMCP
ผู้เผยแพร่ระบุตัวแปรสภาพแวดล้อมเหล่านี้: MEMOFS_API_KEY, MEMOFS_RUNTIME
MemoFS MCP Server เข้าถึงได้ผ่าน HTTP
ตรวจสอบล่าสุดเมื่อ 2026-11-08 Persistent memory and virtual filesystem MCP server for AI agents.