Mem0 Coding Preferences Manager

Mem0 Coding Preferences Manager

An MCP server that integrates with mem0.ai to help users store, retrieve, and search coding preferences for more consistent programming practices.

mem0ai

Remote Shell Execution
Programming Docs Access
AI Memory Systems
AI Content Generation
Visit Server

README

MCP Server with Mem0 for Managing Coding Preferences

This demonstrates a structured approach for using an MCP server with mem0 to manage coding preferences efficiently. The server can be used with Cursor and provides essential tools for storing, retrieving, and searching coding preferences.

Installation

  1. Clone this repository
  2. Initialize the uv environment:
uv venv
  1. Activate the virtual environment:
source .venv/bin/activate
  1. Install the dependencies using uv:
# Install in editable mode from pyproject.toml
uv pip install -e .
  1. Update .env file in the root directory with your mem0 API key:
MEM0_API_KEY=your_api_key_here

Usage

  1. Start the MCP server:
uv run main.py
  1. In Cursor, connect to the SSE endpoint, follow this doc for reference:
http://0.0.0.0:8080/sse
  1. Open the Composer in Cursor and switch to Agent mode.

Demo with Cursor

https://github.com/user-attachments/assets/56670550-fb11-4850-9905-692d3496231c

Features

The server provides three main tools for managing code preferences:

  1. add_coding_preference: Store code snippets, implementation details, and coding patterns with comprehensive context including:

    • Complete code with dependencies
    • Language/framework versions
    • Setup instructions
    • Documentation and comments
    • Example usage
    • Best practices
  2. get_all_coding_preferences: Retrieve all stored coding preferences to analyze patterns, review implementations, and ensure no relevant information is missed.

  3. search_coding_preferences: Semantically search through stored coding preferences to find relevant:

    • Code implementations
    • Programming solutions
    • Best practices
    • Setup guides
    • Technical documentation

Why?

This implementation allows for a persistent coding preferences system that can be accessed via MCP. The SSE-based server can run as a process that agents connect to, use, and disconnect from whenever needed. This pattern fits well with "cloud-native" use cases where the server and clients can be decoupled processes on different nodes.

Server

By default, the server runs on 0.0.0.0:8080 but is configurable with command line arguments like:

uv run main.py --host <your host> --port <your port>

The server exposes an SSE endpoint at /sse that MCP clients can connect to for accessing the coding preferences management tools.

Recommended Servers

Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
React MCP

React MCP

react-mcp integrates with Claude Desktop, enabling the creation and modification of React apps based on user prompts

Featured
Local
AIO-MCP Server

AIO-MCP Server

🚀 All-in-one MCP server with AI search, RAG, and multi-service integrations (GitLab/Jira/Confluence/YouTube) for AI-enhanced development workflows. Folk from

Featured
Local
Persistent Knowledge Graph

Persistent Knowledge Graph

An implementation of persistent memory for Claude using a local knowledge graph, allowing the AI to remember information about users across conversations with customizable storage location.

Featured
Local
OpenRouter MCP Server

OpenRouter MCP Server

Provides integration with OpenRouter.ai, allowing access to various AI models through a unified interface.

Featured
Fetch MCP Server

Fetch MCP Server

Provides functionality to fetch web content in various formats, including HTML, JSON, plain text, and Markdown.

Featured
Search1API MCP Server

Search1API MCP Server

A Model Context Protocol (MCP) server that provides search and crawl functionality using Search1API.

Featured
Supabase MCP Server (used by Deploya.dev)

Supabase MCP Server (used by Deploya.dev)

Enables Cursor and Windsurf to safely interact with Supabase databases by providing tools for database management, SQL query execution, and Supabase Management API access with built-in safety controls.

Featured