Memory OS AI
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Here is a step-by-step guide with screenshots.
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.
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: local-memory-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 |
| Index a folder of documents into FAISS |
| Semantic search across all indexed content |
| Count keyword occurrences across documents |
| Get relevant context for the current task |
| List all indexed documents with stats |
| Transcribe audio files (Whisper) |
| Engine status (index size, model, device) |
| Compact/deduplicate the FAISS index |
| Sync messages from configured chat sources |
| Add a chat source (Claude, ChatGPT, etc.) |
| Remove a chat source |
| Status of all chat sources |
| Auto-detect chat workspaces on disk |
| Full memory briefing for session start |
| Persist conversation messages to memory |
| Link another project's memory |
| Unlink a project |
| List all linked projects |
| Configure cloud storage backend for overflow |
| Show local disk + cloud storage status |
| 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 antiwordInstall
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 statusManual 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 --httpProject 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.mdCloud 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 |
|
|
iCloud Drive | (macOS native, no extra deps) |
|
Dropbox |
|
|
OneDrive | (auto-detects mount) or Graph API |
|
Amazon S3 |
|
|
Azure Blob |
|
|
Box |
|
|
Backblaze B2 |
|
|
All providers |
| — |
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 lowConfiguration
Environment Variables
Variable | Default | Description |
|
| Cache / FAISS index directory |
|
| SentenceTransformer model name |
| (none) | Optional API key for SSE/HTTP auth |
| (none) | Cloud provider name (see table above) |
| (none) | JSON credentials or path to JSON file |
|
| 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 |
Factory for AI agent firms — 28 SKILL.md, 5 SOUL.md, 15 sectors | |
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.
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