@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 runtime for AI agents.
What is MemoFS?
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 .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
├── archive/ # Cold storage for deprecated memories
│ └── <id>.json # Full-fidelity archived memory records
└── snapshots/
└── snap_123.json # Versioned restore checkpointsRelated MCP server: contextforge-mcp
Quick Start
MemoFS serves two primary paths: users running AI agents day-to-day and engineers building custom agents & runtimes.
Path A: For Users of AI Agents (Cursor, Claude Code, Codex, Copilot, Cline, etc.)
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 initYour agent now automatically inherits durable memory across sessions — zero manual prompting required.
Path B: For Builders of AI Agents & Runtimes
Embed the runtime directly into your TypeScript or Node.js agent architecture:
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.
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).
Memory Intelligence
Project & Source Anchoring — bind memories to project files, data schemas, byte hashes, and symbol paths; query-time drift detection automatically demotes stale knowledge when assets change.
Agent Behavior Enforcement — deterministic push hooks across 9+ agent tools (Claude Code, Cursor, Copilot, Codex, OpenCode, Cline, etc.) inject active memory context at session start and preserve it across context compactions.
Causal Lineage & Action Receipts — traverse decision provenance (
memofs why <id>) backed by append-only action receipts with task correlation (taskRef).Ephemeral Coordination Stream — real-time cross-agent pub/sub (
stream.jsonl) with typed coordination events (agent.heartbeat,resource.intent,task.status,agent.hint) filtered out of durable recall.Static Memory Linter —
memofs lintCI/CD rule pipeline detecting broken references, contradictory assertions, and broken provenance links.Cognitive Decay & Cold Archive — kind-specific expiry thresholds transition old memories to
unverifiedstatus before semantic archiving.Session Outcomes & AgentFS — isolated workspace scratchpads with
success/failure/abortedoutcome gates governing durable memory promotion and cleanup.
Packages
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.
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. | |
Canonical JSON schemas, TypeScript contracts, and schema validators. | |
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/ # 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.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 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: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 deployed
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