Inked

A powerful MCP server for memory management with Claude apps. Fast, simple, and optionally enhanced with AI-powered search.

Features

• Fast text search - Lightning-fast memory retrieval by default
• AI-powered search - Optional embedding-based semantic search currently not working as of 06/25/25
• AI reranking - Experimental reranking for even better results
• Simple storage - Plain text storage in SQLite (no encryption overhead)
• Secure - All data stored locally in ~/.inked/

Installation

Option 1: (Recommended)

npm install -g @frgmt/inked

Option 2: Local Development

git clone https://github.com/frgmt/inked.git
cd inked
npm install
npm run build
node dist/index.js

Basic Usage

Add to your MCP server configuration:

Standard (fast text search):

{
  "mcpServers": {
    "inked": {
      "command": "npx",
      "args": ["@frgmt/inked"]
    }
  }
}

^ Use this one. the rest wont work. need to work out some kinks

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With AI embeddings (semantic search):

{
  "mcpServers": {
    "inked": {
      "command": "npx",
      "args": ["@frgmt/inked", "--use-embeddings"]
    }
  }
}

With embeddings + AI reranking (best results):

{
  "mcpServers": {
    "inked": {
      "command": "npx",
      "args": ["@frgmt/inked", "--use-embeddings", "--use-reranking"]
    }
  }
}

Experimental Features

AI-Powered Search (Optional)

Inked supports experimental embedding-based search for more nuanced memory retrieval.

Embedding Models

Flag	Model	Memory Usage	Best For
--use-embeddings	Qwen3-0.6B	~2GB RAM	Short memories, quick responses
--use-embeddings=4b	Qwen3-4B	~8GB RAM	Longer memories, better nuance
--use-embeddings=8b	Qwen3-8B	~16GB RAM	Complex memories, documents

Reranking Models (Requires embeddings)

Flag	Model	Additional Memory	Best For
--use-reranking	Qwen3-Reranker-0.6B	~1GB RAM	Improved relevance
--use-reranking=4b	Qwen3-Reranker-4B	~4GB RAM	Best result quality

How to Choose Models

For most users: Start with no flags (fast text search)

For better semantic understanding: Add --use-embeddings

• Good for finding memories by meaning rather than exact words
• First run downloads ~2GB model (one-time)

For nuanced, longer memories: Use --use-embeddings=4b

• Better at understanding context in longer text
• Handles more complex relationships between ideas

For best results: Add --use-reranking with embeddings

• AI re-scores top candidates for optimal ranking
• Significantly improves search quality

For power users: --use-embeddings=8b --use-reranking=4b

• Best possible search quality
• Requires 20+ GB RAM
• Good for research, documentation, complex projects

Memory Requirements

Configuration	RAM Needed	Download Size	First Launch
Default (text)	~50MB	0MB	Instant
Basic embeddings	~2GB	~1.2GB	2-5 minutes
4B embeddings	~8GB	~4GB	5-10 minutes
8B embeddings	~16GB	~8GB	10-20 minutes
+ Reranking	+1-4GB	+0.5-2GB	+1-3 minutes

Models are cached locally and only downloaded once

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Usage Guide