MCP Memory Service

# Gemini Context: MCP Memory Service ## Project Overview This project is a sophisticated and feature-rich MCP (Memory Component Protocol) server designed to provide a persistent, semantic memory layer for AI assistants, particularly "Claude Desktop". It's built with Python and leverages a variety of technologies to deliver a robust and performant memory service. The core of the project is the `MemoryServer` class, which handles all MCP tool calls. It features a "dream-inspired" memory consolidation system that autonomously organizes and compresses memories over time. The server is built on top of FastAPI, providing a modern and asynchronous API. The project offers two distinct storage backends, allowing users to choose the best fit for their needs: * **ChromaDB:** A feature-rich vector database that provides advanced search capabilities and is well-suited for large memory collections. * **SQLite-vec:** A lightweight, file-based backend that uses the `sqlite-vec` extension for vector similarity search. This is a great option for resource-constrained environments. The project also includes a comprehensive suite of scripts for installation, testing, and maintenance, as well as detailed documentation. ## Building and Running ### Installation The project uses a custom installer that intelligently detects the user's hardware and selects the optimal configuration. To install the project, run the following commands: ```bash # Clone the repository git clone https://github.com/doobidoo/mcp-memory-service.git cd mcp-memory-service # Create and activate a virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Run the intelligent installer python install.py ``` ### Running the Server The server can be run in several ways, depending on the desired configuration. The primary entry point is the `mcp_memory_service.server:main` script, which can be executed as a Python module: ```bash python -m mcp_memory_service.server ``` Alternatively, the `pyproject.toml` file defines a `memory` script that can be used to run the server: ```bash memory ``` The server can also be run as a FastAPI application using `uvicorn`: ```bash uvicorn mcp_memory_service.server:app --host 0.0.0.0 --port 8000 ``` ### Testing The project includes a comprehensive test suite that can be run using `pytest`: ```bash pytest tests/ ``` ## Development Conventions * **Python 3.10+:** The project requires Python 3.10 or higher. * **Type Hinting:** The codebase uses type hints extensively to improve code clarity and maintainability. * **Async/Await:** The project uses the `async/await` pattern for all I/O operations to ensure high performance and scalability. * **PEP 8:** The code follows the PEP 8 style guide. * **Dataclasses:** The project uses dataclasses for data models to reduce boilerplate code. * **Docstrings:** All modules and functions have triple-quoted docstrings that explain their purpose, arguments, and return values. * **Testing:** All new features should be accompanied by tests to ensure they work as expected and don't introduce regressions.