IzumiSy/mcp-duckdb-memory-server

A forked version of the official Knowledge Graph Memory Server.
## Installation
### Installing via Smithery
To install DuckDB Knowledge Graph Memory Server for Claude Desktop automatically via Smithery:
bash
npx -y @smithery/cli install @IzumiSy/mcp-duckdb-memory-server --client claude
### Manual install
Otherwise, add @IzumiSy/mcp-duckdb-memory-server in your claudedesktopconfig.json manually (MEMORYFILEPATH is optional)
bash
"mcpServers": {
"graph-memory": {
"command": "npx",
"args": [
"-y",
"@izumisy/mcp-duckdb-memory-server"
],
"env": {
"MEMORYFILEPATH": "/path/to/your/memory.data"
The data stored on that path is a DuckDB database file.
### Docker
Build
bash
docker build -t mcp-duckdb-graph-memory .
Run
bash
docker run -dit mcp-duckdb-graph-memory
## Usage
Use the example instruction below
Follow these steps for each interaction:
1. User Identification:
You should assume that you are interacting with defaultuser
If you have not identified defaultuser, proactively try to do so.
2. Memory Retrieval:
Always begin your chat by saying only "Remembering..." and search relevant information from your knowledge graph
Create a search query from user words, and search things from "memory". If nothing matches, try to break down words in the query at first ("A B" to "A" and "B" for example).
Always refer to your knowledge graph as your "memory"
3. Memory
While conversing with the user, be attentive to any new information that falls into these categories:
a) Basic Identity (age, gender, location, job title, education level, etc.)
b) Behaviors (interests, habits, etc.)
c) Preferences (communication style, preferred language, etc.)
d) Goals (goals, targets, aspirations, etc.)
e) Relationships (personal and professional relationships up to 3 degrees of separation)
4. Memory Update:
If any new information was gathered during the interaction, update your memory as follows:
a) Create entities for recurring organizations, people, and significant events
b) Connect them to the current entities using relations
b) Store facts about them as observations
## Motivation
This project enhances the original MCP Knowledge Graph Memory Server by replacing its backend with DuckDB.
### Why DuckDB?
The original MCP Knowledge Graph Memory Server used a JSON file as its data store and performed in-memory searches. While this approach works well for small datasets, it presents several challenges:
1. Performance: In-memory search performance degrades as the dataset grows
2. Scalability: Memory usage increases significantly when handling large numbers of entities and relations
3. Query Flexibility: Complex queries and conditional searches are difficult to implement
4. Data Integrity: Ensuring atomicity for transactions and CRUD operations is challenging
DuckDB was chosen to address these challenges:
Fast Query Processing: DuckDB is optimized for analytical queries and performs well even with large datasets
SQL Interface: Standard SQL can be used to execute complex queries easily