@memofs/mcp-server
OfficialSupports Cloudflare R2 for blob storage and Workers AI for graph extraction within the memory store.
Can be deployed on Cloudflare Workers, with an adapter for Workers AI-powered entity extraction.
Connector plugin that ingests content from GitHub repositories/issues into the memory store.
Connector plugin that ingests content from Notion into the memory store for recall.
Allows fully local vector search via onnxruntime, running embeddings in-process without external API keys.
Provides an embeddings adapter for semantic search, enabling vector-based recall within the memory system.
Uses Turso/libSQL as a metadata store for the memory system.
Integrates with Vercel AI SDK to provide runtime bridges and tool definitions for AI agents.
MemoFS
Open-source, file-first memory for AI applications and agents.
What is MemoFS?
File-first memory for AI applications and 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 .memofs/ directory you can cat, git diff, and roll back.
.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
└── snapshots/
└── snap_123.json # Versioned restore checkpointsRelated MCP server: contextforge-mcp
Quick Start
Reach first success in under a minute. No API keys, no database setup, no cloud required.
npm install @memofs/coreimport { 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.
To connect your coding agent (Cursor, Claude Code, etc.), use the stdio-compatible @memofs/mcp-server.
Architecture
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 CloudThe 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).
Packages
MemoFS is structured as a monorepo containing 15 published public packages under the @memofs/ scope. The CLI ships as @memofs/cli and installs the memofs command.
Core Engine & Servers
Package | Purpose |
Core runtime, virtual AgentFS, graph engine, and hybrid recall router. | |
CLI tool for local and cloud memory workflows ( | |
Self-hostable, OSS-deployable memory server for Node and Workers. | |
Model Context Protocol server exposing memory tools to AI agents. | |
Local ingestion framework plugins (Notion, GitHub). | |
Message schemas and validation for JSON-RPC 2.0. |
Providers & Adapters
Package | Purpose |
Vercel AI SDK integration, runtime bridges, and tool definitions. | |
OpenAI embeddings adapter. | |
Voyage AI embedder and reranker adapter. | |
ONNX local embedder (Transformers.js) for zero-API-key hybrid recall. | |
Cloudflare Workers AI graph extractor adapter. | |
Cloudflare R2 Blob storage adapter. | |
Turso / libSQL metadata store adapter. |
Development Tooling
Package | Purpose |
Shared contract tests, mocks, fakes, and fixtures. | |
Benchmark workloads and runners. |
Open Source vs. MemoFS Cloud
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.
Feature | Open source (this repo) | MemoFS Cloud |
Local file-first memory | ✅ | ✅ |
CLI + stdio MCP server | ✅ | ✅ |
All adapters (OpenAI, Voyage, etc.) | ✅ | ✅ |
Hosted sync (keep memory in sync) | ✅ client | ✅ hosted |
Team workspaces & access control | — | ✅ available |
Memory dashboard (explore, consolidate) | — | ✅ available |
Hosted managed MCP endpoint | — | ✅ available (Pro+) |
Managed runtime (memory API over HTTPS) | — | Soon |
Repository Structure
memofs/
├── apps/
│ └── docs/ # VitePress documentation (docs.memofs.dev)
├── packages/ # 15 published @memofs/* packages
├── tooling/ # Private @repo/* workspace build packages
├── benchmarks/ # Workspace benchmarking suite
├── examples/ # Runnable examples
└── package.jsonWorkspace Commands
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 checks (Biome)
pnpm format-and-lint
# Fix linting and formatting issues automatically
pnpm format-and-lint:fix
# Build documentation locally
pnpm docs:buildContributing
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.
License
MIT. See LICENSE.
This server cannot be installed
Maintenance
Related MCP Servers
- AlicenseCqualityCmaintenanceMCP server for sharing source-backed engineering memory across AI coding clients like Cursor and VS Code.Last updated301MIT
- AlicenseBqualityBmaintenancePersistent memory MCP server for Claude Code, Cursor, and GitHub Copilot. Semantic search, Git sync, project-based, Persistent memory MCP server for Claude Code, Cursor, and GitHub Copilot. Semantic search, Git sync, project-based organization, and team collaboration via Model Context Protocol.Last updated672662MIT
- Alicense-qualityAmaintenanceOpen-source MCP memory server providing persistent, cross-platform context for AI tools via a knowledge graph with encrypted storage.Last updated1813AGPL 3.0
- Flicense-qualityBmaintenanceA persistent, conflict-aware memory MCP server for AI coding assistants (Cursor, Claude Code).Last updated
Related MCP Connectors
User-owned memory for AI agents, Copilot, Claude, IDEs, CLIs, and chat apps over remote MCP.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Person-owned, portable AI memory as a remote MCP server, readable and writable by any MCP client.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/memo-fs/memofs'
If you have feedback or need assistance with the MCP directory API, please join our Discord server