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    Memory Plus

    🧠 π‘΄π’†π’Žπ’π’“π’š-𝑷𝒍𝒖𝒔 is a lightweight, local RAG memory store for MCP agents. Easily record, retrieve, update, delete, and visualize persistent "memories" across sessionsβ€”perfect for developers working with multiple AI coders (like Windsurf, Cursor, or Copilot) or anyone who wants their AI to actually remember them.

    41 stars
    Python
    Updated Oct 17, 2025
    agent
    hackathon
    mcp
    mcp-server
    memory
    memory-store
    model-context-protocol

    Table of Contents

    • Key Features
    • Installation
    • 1. Prerequisites
    • VS Code One-Click Setup
    • Local Testing and Development
    • RoadMap
    • License
    • FAQ
    • 1. Why is memory-plus not working?
    • 2. How do I use memory-plus in a real chat session?

    Table of Contents

    • Key Features
    • Installation
    • 1. Prerequisites
    • VS Code One-Click Setup
    • Local Testing and Development
    • RoadMap
    • License
    • FAQ
    • 1. Why is memory-plus not working?
    • 2. How do I use memory-plus in a real chat session?

    Documentation

    memory_plus

    pretty image

    License: MIT visitors PyPI version PyPI Downloads

    Memory-Plus

    A lightweight, local Retrieval-Augmented Generation (RAG) memory store for MCP agents. Memory-Plus lets your agent record, retrieve, update, and visualize persistent "memories"β€”notes, ideas, and session contextβ€”across runs.

    πŸ† First Place at the Infosys Cambridge AI Centre Hackathon!

    Key Features

    • Record Memories:Save user data, ideas, and important context.
    • Retrieve Memories:Search by keywords or topics over past entries.
    • Recent Memories:Fetch the last *N* items quickly.
    • Update Memories:Append or modify existing entries seamlessly.
    • Visualize Memories:Interactive graph clusters revealing relationships.
    • File Import (*since v0.1.2*):Ingest documents directly into memory.
    • Delete Memories (*since v0.1.2*):Remove unwanted entries.
    • Memory for Memories (*since v0.1.4*):Now we use resources to teach your AI exactly when (and when not) to recall past interactions.
    • Memory Versioning (*since v0.1.4*):When memories are updated, we keep the old versions to provide a full history.

    ---

    alt text

    Installation

    1. Prerequisites

    Google API Key

    Obtain from Google AI Studio and set as GOOGLE_API_KEY in your environment.

    Note that we will only use the Gemini Embedding API with this API key, so it is Entirely Free for you to use!

    Setup Google API Key Example

    bash
    # macOS/Linux
      export GOOGLE_API_KEY=""
    
      # Windows (PowerShell)
      setx GOOGLE_API_KEY ""

    UV Runtime

    Required to serve the MCP plugin.

    Install UV Runtime

    bash
    pip install uv

    Or install via shell scripts:

    bash
    # macOS/Linux
    curl -LsSf https://astral.sh/uv/install.sh | sh
    
    # Windows (PowerShell)
    powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

    VS Code One-Click Setup

    Click the badge below to automatically install and configure Memory-Plus in VS Code:

    One Click Install in VS Code

    This will add the following to your settings.json:

    json
    {
        "mcpServers": {
          //...,  your other MCP servers
          "memory-plus": {
            "command": "uvx",
            "args": [
              "-q",
              "memory-plus@latest"
            ],
          }
        }
      }

    For cursor, go to file -> Preferences -> Cursor Settings -> MCP and add the above config.

    If you didn't add the GOOGLE_API_KEY to your secrets / environment variables, you can add it with:

    json
    "env": {
            "GOOGLE_API_KEY": ""
          }

    just after the args array with in the memory-plus dictionary.

    For Cline add the following to your cline_mcp_settings.json:

    json
    {
      "mcpServers": {
        //...,  your other MCP servers
        "memory-plus": {
          "disabled": false,
          "timeout": 300,
          "command": "uvx",
          "args": [
            "-q",
            "memory-plus@latest"
          ],
          "env": {
            "GOOGLE_API_KEY": "${{ secrets.GOOGLE_API_KEY }}"
          },
          "transportType": "stdio"
        }
      }
    }

    For other IDEs it should be mostly similar to the above.

    Local Testing and Development

    Using MCP Inspector, you can test the memory-plus server locally.

    bash
    git clone https://github.com/Yuchen20/Memory-Plus.git
    cd Memory-Plus
    npx @modelcontextprotocol/inspector fastmcp run run .\\memory_plus\\mcp.py

    Or If you prefer using this MCP in an actual Chat Session. There is a template chatbot in agent.py.

    bash
    # Clone the repository
    git clone https://github.com/Yuchen20/Memory-Plus.git
    cd Memory-Plus
    
    # Install dependencies
    pip install uv
    uv pip install fast-agent-mcp
    uv run fast-agent setup

    setup the fastagent.config.yaml and fastagent.secrets.yaml with your own API keys.

    bash
    # Run the agent
    uv run agent_memory.py

    RoadMap

    • [x] Memory Update
    • [x] Improved prompt engineering for memory recording
    • [x] Better Visualization of Memory Graph
    • [x] File Import
    • [ ] Remote backup!
    • [ ] Web UI for Memory Management

    If you have any feature requests, please feel free to add them by adding a new issue or by adding a new entry in the Feature Request

    License

    This project is licensed under the Apache License 2.0. See LICENSE for details.

    FAQ

    1. Why is memory-plus not working?

    • Memory-plus has a few dependencies that can be slow to download the first time. It typically takes around 1 minute to fetch everything needed.
    • Once dependencies are installed, subsequent usage will be much faster.
    • If you experience other issues, please feel free to open a new issue on the repository.

    2. How do I use memory-plus in a real chat session?

    • Simply add the MCP JSON file to your MCP setup.
    • Once added, memory-plus will automatically activate when needed.

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