Duckdb Memory
    Duckdb Memory

    Duckdb Memory

    MCP Memory Server with DuckDB backend

    4.3

    GitHub Stats

    Stars

    41

    Forks

    8

    Release Date

    6/14/2025

    about three weeks ago

    Detailed Description

    MCP DuckDB Knowledge Graph Memory Server

    Test smithery badge NPM Version NPM License

    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:

    npx -y @smithery/cli install @IzumiSy/mcp-duckdb-memory-server --client claude
    

    Manual install

    Otherwise, add @IzumiSy/mcp-duckdb-memory-server in your claude_desktop_config.json manually (MEMORY_FILE_PATH is optional)

    {
      "mcpServers": {
        "graph-memory": {
          "command": "npx",
          "args": [
            "-y",
            "@izumisy/mcp-duckdb-memory-server"
          ],
          "env": {
            "MEMORY_FILE_PATH": "/path/to/your/memory.data"
          }
        }
      }
    }
    

    The data stored on that path is a DuckDB database file.

    Docker

    Build

    docker build -t mcp-duckdb-graph-memory .
    

    Run

    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 default_user
       - If you have not identified default_user, 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
    • Transaction Support: Supports transaction processing to maintain data integrity
    • Indexing Capabilities: Allows creation of indexes to improve search performance
    • Embedded Database: Works within the application without requiring an external database server

    Implementation Details

    This implementation uses DuckDB as the backend storage system, focusing on two key aspects:

    Database Structure

    The knowledge graph is stored in a relational database structure as shown below:

    erDiagram
        ENTITIES {
            string name PK
            string entityType
        }
        OBSERVATIONS {
            string entityName FK
            string content
        }
        RELATIONS {
            string from_entity FK
            string to_entity FK
            string relationType
        }
    
        ENTITIES ||--o{ OBSERVATIONS : "has"
        ENTITIES ||--o{ RELATIONS : "from"
        ENTITIES ||--o{ RELATIONS : "to"
    

    This schema design allows for efficient storage and retrieval of knowledge graph components while maintaining the relationships between entities, observations, and relations.

    Fuzzy Search Implementation

    The implementation combines SQL queries with Fuse.js for flexible entity searching:

    • DuckDB SQL queries retrieve the base data from the database
    • Fuse.js provides fuzzy matching capabilities on top of the retrieved data
    • This hybrid approach allows for both structured queries and flexible text matching
    • Search results include both exact and partial matches, ranked by relevance

    Development

    Setup

    pnpm install
    

    Testing

    pnpm test
    

    License

    This project is licensed under the MIT License - see the LICENSE file for details.

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