inked

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

Auto-Memory Setup

Add this to your Claude settings/preferences:

"At the start of new conversations, use the inked Read tool with 'ALL' to load my memories. Only mention memories when directly relevant to our conversation. Use the Write tool to save important preferences, facts, or insights that should be remembered for future conversations."

How It Works

Read once per conversation: Memories stay in context after initial load
Silent operation: Claude uses memories without mentioning them unless relevant
Smart writing: Automatically saves important information for future sessions

When to Write Memories

User preferences and communication style
Important project information and context
Recurring topics or themes
Facts that should persist across conversations
Insights or patterns worth remembering

Tools

`read`

Search and retrieve memories.

Parameters:

search (required): Query string or "ALL" for everything
topr (optional): Number of results (1-5, default: 3)

`write`

Add or delete memories.