@vestige/mcp

Vestige MCP Server - A synthetic hippocampus for AI assistants.

Built on 130 years of cognitive science research, Vestige provides biologically-inspired memory that decays, strengthens, and consolidates like the human mind.

Installation

npm install -g vestige-mcp-server

This automatically downloads the correct binary for your platform (macOS, Linux, Windows) from GitHub releases.

What gets installed

| Command | Description | |---------|-------------| | vestige-mcp | MCP server for Claude integration | | vestige | CLI for stats, health checks, and maintenance |

Verify installation

vestige health

Usage with Claude Code

claude mcp add vestige vestige-mcp -s user

Then restart Claude.

Usage with Claude Desktop

Add to your Claude Desktop configuration:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "vestige": {
      "command": "vestige-mcp"
    }
  }
}

CLI Commands

vestige stats          # Memory statistics
vestige stats --states # Cognitive state distribution
vestige health         # System health check
vestige consolidate    # Run memory maintenance cycle

Features

• FSRS-6 Algorithm: State-of-the-art spaced repetition for optimal memory retention
• Dual-Strength Memory: Bjork & Bjork (1992) - Storage + Retrieval strength model
• Synaptic Tagging: Memories become important retroactively (Frey & Morris 1997)
• Semantic Search: Local embeddings via nomic-embed-text-v1.5 (768 dimensions)
• Local-First: All data stays on your machine - no cloud, no API costs

Storage & Memory

Vestige uses SQLite for storage. Your memories are stored on disk, not in RAM.

• Database limit: 216TB (SQLite theoretical max)
• RAM usage: ~64MB cache (configurable)
• Typical usage: 1 million memories ≈ 1-2GB on disk

You'll never run out of space. A heavy user creating 100 memories/day would use ~1.5GB after 10 years.

Embeddings

On first use, Vestige downloads the nomic-embed-text-v1.5 model (~130MB). This is a one-time download and all subsequent operations are fully offline.

The model is stored in .fastembed_cache/ in your working directory, or you can set a global location:

export FASTEMBED_CACHE_PATH="$HOME/.fastembed_cache"

Environment Variables

| Variable | Description | Default | |----------|-------------|---------| | VESTIGE_DATA_DIR | Data storage directory | ~/.vestige | | VESTIGE_LOG_LEVEL | Log verbosity | info | | FASTEMBED_CACHE_PATH | Embeddings model location | ./.fastembed_cache |

Troubleshooting

"Could not attach to MCP server vestige"

1. Verify binary exists: which vestige-mcp
2. Test directly: vestige-mcp (should wait for stdio input)
3. Check Claude logs: ~/Library/Logs/Claude/ (macOS)

"vestige: command not found"

Reinstall the package:

npm install -g vestige-mcp-server

Embeddings not downloading

The model downloads on first ingest or search operation. If Claude can't connect to the MCP server, no memory operations happen and no model downloads.

Fix the MCP connection first, then the model will download automatically.

Supported Platforms

| Platform | Architecture | |----------|--------------| | macOS | ARM64 (Apple Silicon) | | Linux | x86_64 | | Windows | x86_64 |

License

MIT