MCP-Mem0: Long-Term Memory for AI Agents

@gustavoserafim

About MCP-Mem0: Long-Term Memory for AI Agents

No overview available yet

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mem0-mcp-server": {
      "command": "uv",
      "args": [
        "pip",
        "install",
        "-e",
        "."
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCP-Mem0?

MCP-Mem0 is a reference implementation of an MCP (Model Context Protocol) server integrated with the Mem0 memory platform, providing AI agents with persistent, searchable long-term memory. It is designed for developers building MCP-compatible agents that need to store, retrieve, and semantically search information across sessions.

How to use MCP-Mem0?

Clone the repository, install dependencies with uv or build the Docker image, configure environment variables (transport, LLM provider, database URL), and run the server via uv run src/main.py or docker run. Connect any MCP client by pointing it to the SSE endpoint or by configuring stdio transport in the client's MCP settings.

Key features of MCP-Mem0

Semantic indexing of stored information for relevance-based retrieval
Three memory management tools: save, list all, and search memories
Supports SSE and stdio transport protocols
Compatible with multiple LLM providers (OpenAI, OpenRouter, Ollama)
Docker-based deployment for containerized environments

Use cases of MCP-Mem0

AI assistants that need to remember user preferences and conversation history across sessions
Agents performing multi-step research that require persistent context
Knowledge management tools that organize and retrieve information via natural language queries

FAQ from MCP-Mem0

What dependencies does MCP-Mem0 require?

Python 3.12+, a PostgreSQL database (with Supabase supported) for vector storage, and API keys for your chosen LLM provider (OpenAI, OpenRouter, or Ollama). Docker is recommended for containerized deployments.

Which transports does MCP-Mem0 support?

SSE (Server-Sent Events) and stdio (standard input/output) transport protocols are both supported, configurable via the TRANSPORT environment variable.

What LLM providers and models can I use with MCP-Mem0?

OpenAI, OpenRouter, and Ollama are supported. The provider, base URL, API key, model, and embedding model are all configurable through environment variables.

How does MCP-Mem0 store and retrieve memories?

Memories are stored with semantic indexing using embedding vectors in a PostgreSQL database. The server offers three tools: save_memory to store information, get_all_memories to retrieve everything, and search_memories to find relevant memories via semantic search.

How do I connect an MCP client to MCP-Mem0?

For SSE transport, use the URL http://localhost:8050/sse (adjust host/port as needed). For stdio transport, configure the client to run the Python script or Docker container with the required environment variables.

Frequently asked questions

What dependencies does MCP-Mem0 require?

Which transports does MCP-Mem0 support?

SSE (Server-Sent Events) and stdio (standard input/output) transport protocols are both supported, configurable via the `TRANSPORT` environment variable.

What LLM providers and models can I use with MCP-Mem0?

OpenAI, OpenRouter, and Ollama are supported. The provider, base URL, API key, model, and embedding model are all configurable through environment variables.

How does MCP-Mem0 store and retrieve memories?

Memories are stored with semantic indexing using embedding vectors in a PostgreSQL database. The server offers three tools: `save_memory` to store information, `get_all_memories` to retrieve everything, and `search_memories` to find relevant memories via semantic search.

How do I connect an MCP client to MCP-Mem0?

For SSE transport, use the URL `http://localhost:8050/sse` (adjust host/port as needed). For stdio transport, configure the client to run the Python script or Docker container with the required environment variables.

Comments

Basic information

More Memory & Knowledge MCP servers

Ultimate Google Docs & Drive MCP Server

a-bonus

The Ultimate Google Docs, Sheets, Drive, Gmail, & Google Calendar MCP Server. This MCP (primarily for use in Claude Desktop) gains full access to your google suite and lets claude do its thing.

605

Context Portal MCP (ConPort)

GreatScottyMac

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.

763