iflow-mcp_mem0-mcp

<h1 align="center">MCP-Mem0: Long-Term Memory for AI Agents</h1> <p align="center"> <img src="public/Mem0AndMCP.png" alt="Mem0 and MCP Integration" width="600"> </p> A template implementation of the [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server integrated with [Mem0](https://mem0.ai) for providing AI agents with persistent memory capabilities. Use this as a reference point to build your MCP servers yourself, or give this as an example to an AI coding assistant and tell it to follow this example for structure and code correctness! ## Overview This project demonstrates how to build an MCP server that enables AI agents to store, retrieve, and search memories using semantic search. It serves as a practical template for creating your own MCP servers, simply using Mem0 and a practical example. The implementation follows the best practices laid out by Anthropic for building MCP servers, allowing seamless integration with any MCP-compatible client. ## Features The server provides three essential memory management tools: 1. **`save_memory`**: Store any information in long-term memory with semantic indexing 2. **`get_all_memories`**: Retrieve all stored memories for comprehensive context 3. **`search_memories`**: Find relevant memories using semantic search ## Prerequisites - Python 3.12+ - Supabase or any PostgreSQL database (for vector storage of memories) - API keys for your chosen LLM provider (OpenAI, OpenRouter, or Ollama) - Docker if running the MCP server as a container (recommended) ## Installation ### Using uv 1. Install uv if you don't have it: ```bash pip install uv ``` 2. Clone this repository: ```bash git clone https://github.com/coleam00/mcp-mem0.git cd mcp-mem0 ``` 3. Install dependencies: ```bash uv pip install -e . ``` 4. Create a `.env` file based on `.env.example`: ```bash cp .env.example .env ``` 5. Configure your environment variables in the `.env` file (see Configuratio…