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Built with ❤️ by Krishna Goyal

    Simple Memory Extension Mcp Server

    An MCP server to extend the context of agents. Useful when coding big features or vibe coding and need to store/recall progress, key moments or changes or anything worth remembering. Simply ask the agent to store memories and recall whenever you want.

    9 stars
    TypeScript
    Updated Sep 10, 2025

    Table of Contents

    • Usage
    • Starting the Server
    • MCP setup
    • Available Tools
    • Context Item Management
    • Namespace Management
    • Semantic Search
    • Semantic Search Implementation
    • Development
    • .env
    • Semantic Search
    • Setup
    • Embedding Model
    • Notes

    Table of Contents

    • Usage
    • Starting the Server
    • MCP setup
    • Available Tools
    • Context Item Management
    • Namespace Management
    • Semantic Search
    • Semantic Search Implementation
    • Development
    • .env
    • Semantic Search
    • Setup
    • Embedding Model
    • Notes

    Documentation

    Simple Memory Extension MCP Server

    An MCP server to extend the context window / memory of agents. Useful when coding big features or vibe coding and need to store/recall progress, key moments or changes or anything worth remembering. Simply ask the agent to store memories and recall whenever you need or ask the agent to fully manage its memory (through cursor rules for example) however it sees fit.

    Usage

    Starting the Server

    bash
    npm install
    npm start

    MCP setup

    json
    "mcpServers": {
        "simple-memory": {
          "url": "http://localhost:3000/sse",
          "trust": false
        }
      }

    Available Tools

    Context Item Management

    • store_context_item - Store a value with key in namespace
    • retrieve_context_item_by_key - Get value by key
    • delete_context_item - Delete key-value pair

    Namespace Management

    • create_namespace - Create new namespace
    • delete_namespace - Delete namespace and all contents
    • list_namespaces - List all namespaces
    • list_context_item_keys - List keys in a namespace

    Semantic Search

    • retrieve_context_items_by_semantic_search - Find items by meaning

    Semantic Search Implementation

    1. Query converted to vector using E5 model

    2. Text automatically split into chunks for better matching

    3. Cosine similarity calculated between query and stored chunks

    4. Results filtered by threshold and sorted by similarity

    5. Top matches returned with full item values

    Development

    bash
    # Dev server
    npm run dev
    
    # Format code
    npm run format

    .env

    code
    # Path to SQLite database file
    DB_PATH=./data/context.db
    
    PORT=3000
    
    # Use HTTP SSE or Stdio
    USE_HTTP_SSE=true
    
    # Logging Configuration: debug, info, warn, error
    LOG_LEVEL=info

    Semantic Search

    This project includes semantic search capabilities using the E5 embedding model from Hugging Face. This allows you to find context items based on their meaning rather than just exact key matches.

    Setup

    The semantic search feature requires Python dependencies, but these *should be* automatically installed when you run: npm run start

    Embedding Model

    We use the intfloat/multilingual-e5-large-instruct

    Notes

    Developed mostly while vibe coding, so don't expect much :D. But it works, and I found it helpful so w/e. Feel free to contribute or suggest improvements.

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