How do I use Memory OS AI?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Memory OS AI search my project notes for deployment steps" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Memory OS AI

by romainsantoli-web

Overview Schema Source Related Servers Discussions Admin

Python

Memory OS AI

Adaptive memory system for AI agents — universal MCP server for Claude Code, Codex CLI, VS Code Copilot, ChatGPT, and any MCP-compatible client.

Tests Coverage Python MCP VS Code Version License

Concept

Memory OS AI transforms your local documents (PDF, DOCX, images, audio) into a semantic memory queryable by any AI model through the MCP (Model Context Protocol).

┌──────────────────────────────────┐
│  AI Client (any MCP-compatible)  │
│  Claude Code / Codex / Copilot   │
│  ChatGPT / custom agents         │
├──────────────────────────────────┤
│         MCP Protocol             │
│   stdio / SSE / Streamable HTTP  │
├──────────────────────────────────┤
│      Memory OS AI Server         │
│  ┌────────┐  ┌───────────────┐   │
│  │ FAISS  │  │ Chat Extractor│   │
│  │ Index  │  │ (4 sources)   │   │
│  └────────┘  └───────────────┘   │
│  ┌────────────────────────────┐  │
│  │ Cross-Project Linking      │  │
│  └────────────────────────────┘  │
└──────────────────────────────────┘

Related MCP server: Graph-Mem MCP

Features

21 MCP tools for memory management, search, chat persistence, project linking, and cloud storage
Semantic search with FAISS + SentenceTransformers (all-MiniLM-L6-v2)
Multi-format ingestion: PDF, DOCX, TXT, images (OCR), audio (Whisper), PPTX
Chat extraction: auto-detects Claude, ChatGPT, Copilot, and terminal history
Cross-project linking: share memory across multiple workspaces
Cloud storage overflow: auto-backup to Google Drive, iCloud, Dropbox, OneDrive, S3, Azure, Box, B2
3 transports: stdio (default), SSE (--sse), Streamable HTTP (--http)
MCP Resources: memory://documents/*, memory://logs/conversation, memory://linked/*
Local-first: all data on your machine by default, cloud only when disk runs low

21 MCP Tools

Tool	Description
`memory_ingest`	Index a folder of documents into FAISS
`memory_search`	Semantic search across all indexed content
`memory_search_occurrences`	Count keyword occurrences across documents
`memory_get_context`	Get relevant context for the current task
`memory_list_documents`	List all indexed documents with stats
`memory_transcribe`	Transcribe audio files (Whisper)
`memory_status`	Engine status (index size, model, device)
`memory_compact`	Compact/deduplicate the FAISS index
`memory_chat_sync`	Sync messages from configured chat sources
`memory_chat_source_add`	Add a chat source (Claude, ChatGPT, etc.)
`memory_chat_source_remove`	Remove a chat source
`memory_chat_status`	Status of all chat sources
`memory_chat_auto_detect`	Auto-detect chat workspaces on disk
`memory_session_brief`	Full memory briefing for session start
`memory_chat_save`	Persist conversation messages to memory
`memory_project_link`	Link another project's memory
`memory_project_unlink`	Unlink a project
`memory_project_list`	List all linked projects
`memory_cloud_configure`	Configure cloud storage backend for overflow
`memory_cloud_status`	Show local disk + cloud storage status
`memory_cloud_sync`	Push/pull/auto-sync between local and cloud

Quick Start

Prerequisites

Python 3.10+
Optional: tesseract (OCR), ffmpeg (audio), antiword (legacy .doc)

# macOS
brew install tesseract ffmpeg antiword

# Ubuntu/Debian
sudo apt-get install tesseract-ocr ffmpeg antiword

Install

git clone https://github.com/romainsantoli-web/Memory-os-ai.git
cd Memory-os-ai
pip install -e ".[dev,audio]"

Auto-Setup (recommended)

# Setup for your AI client:
memory-os-ai setup claude-code    # Claude Code
memory-os-ai setup codex          # Codex CLI
memory-os-ai setup vscode         # VS Code Copilot
memory-os-ai setup claude-desktop # Claude Desktop
memory-os-ai setup chatgpt        # ChatGPT (manual bridge)
memory-os-ai setup all            # All of the above

# Check status:
memory-os-ai setup status

Manual Start

# stdio (default — Claude Code, VS Code, Codex)
memory-os-ai

# SSE transport (port 8765)
memory-os-ai --sse

# Streamable HTTP (port 8765)
memory-os-ai --http

Project Structure

Memory-os-ai/
├── src/memory_os_ai/
│   ├── __init__.py          # Public API: MemoryEngine, ChatExtractor, TOOL_MODELS
│   ├── __main__.py          # python -m memory_os_ai entry point
│   ├── server.py            # MCP server — 21 tools, 3 transports, resources
│   ├── engine.py            # FAISS engine — indexing, search, compact, session brief
│   ├── cloud_storage.py     # 8 cloud backends (GDrive, iCloud, Dropbox, OneDrive, S3, Azure, Box, B2)
│   ├── storage_router.py    # Smart routing: local-first with cloud overflow
│   ├── models.py            # 21 Pydantic models + TOOL_MODELS registry
│   ├── chat_extractor.py    # 4 extractors: Claude, ChatGPT, Copilot, terminal
│   ├── instructions.py      # MEMORY_INSTRUCTIONS for AI clients
│   └── setup.py             # Auto-setup CLI for 5 AI clients
├── bridges/
│   ├── claude-code/         # CLAUDE.md with memory rules
│   ├── claude-desktop/      # config.json for Claude Desktop
│   ├── codex/               # AGENTS.md for Codex CLI
│   ├── vscode/              # mcp.json for VS Code
│   └── chatgpt/             # mcp-connection.json for ChatGPT
├── tests/                   # 410+ tests — 96% coverage
│   ├── test_memory.py       # Engine + models (60 tests)
│   ├── test_chat_extractor.py  # Chat extraction (39 tests)
│   ├── test_bridges.py      # Bridge configs (22 tests)
│   ├── test_gaps.py         # Compact, cross-project, resources (34 tests)
│   ├── test_server_dispatch.py # Server dispatch + async (61 tests)
│   ├── test_setup.py        # Setup CLI targets
│   ├── test_z_coverage_boost.py # Coverage boost (35 tests)
│   └── test_zz_full_coverage.py # Full coverage (97 tests)
├── pyproject.toml           # v3.1.0 — deps, scripts, coverage config + cloud optional deps
├── Dockerfile               # Container deployment
└── README.md

Cloud Storage (v3.1.0)

When local disk runs low (< 500 MB free by default), memory data automatically overflows to a configured cloud backend.

Supported Providers

Provider	Install	Credentials
Google Drive	`pip install memory-os-ai[cloud-gdrive]`	`credentials_json` or `token_json` + `folder_id`
iCloud Drive	(macOS native, no extra deps)	`container` name (default: `memory-os-ai`)
Dropbox	`pip install memory-os-ai[cloud-dropbox]`	`access_token` + `folder`
OneDrive	(auto-detects mount) or Graph API	`mount_path` or `access_token`
Amazon S3	`pip install memory-os-ai[cloud-s3]`	`bucket`, `aws_access_key_id`, `aws_secret_access_key`
Azure Blob	`pip install memory-os-ai[cloud-azure]`	`connection_string` + `container`
Box	`pip install memory-os-ai[cloud-box]`	`access_token` + `folder_id`
Backblaze B2	`pip install memory-os-ai[cloud-b2]`	`application_key_id`, `application_key`, `bucket_name`
All providers	`pip install memory-os-ai[cloud-all]`	—

Usage

# Configure via environment (auto-activates on server start)
export MEMORY_CLOUD_PROVIDER=icloud
export MEMORY_CLOUD_CONFIG='{"container": "memory-os-ai"}'
memory-os-ai

# Or configure at runtime via MCP tool:
#   memory_cloud_configure(provider="s3", credentials={"bucket": "my-bucket", ...})
#   memory_cloud_status()       → local disk + cloud usage
#   memory_cloud_sync("push")   → backup to cloud
#   memory_cloud_sync("pull")   → restore from cloud
#   memory_cloud_sync("auto")   → offload if disk low

Configuration

Environment Variables

Variable	Default	Description
`MEMORY_CACHE_DIR`	`~/.memory-os-ai`	Cache / FAISS index directory
`MEMORY_MODEL`	`all-MiniLM-L6-v2`	SentenceTransformer model name
`MEMORY_API_KEY`	(none)	Optional API key for SSE/HTTP auth
`MEMORY_CLOUD_PROVIDER`	(none)	Cloud provider name (see table above)
`MEMORY_CLOUD_CONFIG`	(none)	JSON credentials or path to JSON file
`MEMORY_DISK_THRESHOLD`	`524288000`	Bytes free before cloud overflow (500 MB)

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

# Run with coverage
python -m pytest tests/ --cov=memory_os_ai --cov-report=term-missing

# Coverage threshold: 80% (enforced in pyproject.toml)

License

GNU Lesser General Public License v3.0 (LGPL-3.0). See LICENSE for details.

For commercial licensing, contact romainsantoli@gmail.com.

Part of the OpenClaw Ecosystem

Memory OS AI is designed to work alongside the OpenClaw agent infrastructure:

Repo	Description
setup-vs-agent-firm	Factory for AI agent firms — 28 SKILL.md, 5 SOUL.md, 15 sectors
mcp-openclaw-extensions	115 MCP tools — security audit, A2A bridge, fleet management
Memory OS AI (this repo)	Semantic memory + chat persistence — universal MCP bridge

Together they form a complete stack: memory (this repo) → skills & souls (setup-vs-agent-firm) → security & orchestration (mcp-openclaw-extensions).

Contributing

Contributions welcome! See CONTRIBUTING.md for guidelines.

⚠️ Contenu généré par IA — validation humaine requise avant utilisation.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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Memory OS AI

Concept

Features

21 MCP Tools

Quick Start

Prerequisites

Install

Auto-Setup (recommended)

Manual Start

Project Structure

Cloud Storage (v3.1.0)

Supported Providers

Usage

Configuration

Environment Variables

Development

License

Part of the OpenClaw Ecosystem

Contributing

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Cognio

Graph-Mem MCP

Selti

agenticmemory