MCP.so
Sign In

MCP-Mem0: Long-Term Memory for AI Agents

@coleam00

Repository

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

MCP server for long term agent memory with Mem0. Also useful as a template to get you started building your own MCP server with Python!

#mcp-mem0#long-term-memory#ai-agents

Config

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

{
  "mcpServers": {
    "mcp-mem0": {
      "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 an implementation of the Model Context Protocol (MCP) server integrated with Mem0, providing AI agents with persistent memory capabilities. It enables agents to store, retrieve, and search memories using semantic search, serving as a practical template for building custom MCP servers.

How to use MCP-Mem0?

Install dependencies with uv pip install -e . or build a Docker image. Configure a .env file with transport (sse or stdio), LLM provider (OpenAI, OpenRouter, or Ollama), embedding model, and a PostgreSQL database URL. Run the server directly with uv run src/main.py or via Docker. Then integrate with any MCP-compatible client (e.g., Claude Desktop, Windsurf) by adding the server configuration JSON with either SSE or stdio transport.

Key features of MCP-Mem0

  • Three memory management tools: save, retrieve, and search memories
  • Semantic search for finding relevant memories
  • Supports OpenAI, OpenRouter, and Ollama LLM providers
  • Deployable via Docker (recommended) or with uv
  • Works with SSE or stdio transport protocols

Use cases of MCP-Mem0

  • Give AI agents long-term memory that persists across conversations
  • Retrieve comprehensive context from all stored memories
  • Find semantically relevant memories for dynamic recall
  • Build custom MCP servers using this as a reference template

FAQ from MCP-Mem0

What are the prerequisites for running MCP-Mem0?

Python 3.12+, a Supabase or PostgreSQL database for vector storage, API keys for your chosen LLM provider, and optionally Docker.

What memory tools does MCP-Mem0 provide?

It provides three tools: save_memory to store information with semantic indexing, get_all_memories to retrieve all stored memories, and search_memories to find relevant memories via semantic search.

Which transport protocols are supported?

MCP-Mem0 supports both SSE (Server-Sent Events) and stdio transport, configurable via the TRANSPORT environment variable.

Where are the memories stored?

Memories are stored as vectors in a PostgreSQL database (Supabase recommended), configured through the DATABASE_URL environment variable.

How do I integrate MCP-Mem0 with Windsurf or n8n?

For Windsurf use serverUrl instead of url in the MCP configuration. For n8n, replace localhost with host.docker.internal in the SSE URL.

Frequently asked questions

What are the prerequisites for running MCP-Mem0?

Python 3.12+, a Supabase or PostgreSQL database for vector storage, API keys for your chosen LLM provider, and optionally Docker.

What memory tools does MCP-Mem0 provide?

It provides three tools: `save_memory` to store information with semantic indexing, `get_all_memories` to retrieve all stored memories, and `search_memories` to find relevant memories via semantic search.

Which transport protocols are supported?

MCP-Mem0 supports both SSE (Server-Sent Events) and stdio transport, configurable via the `TRANSPORT` environment variable.

Where are the memories stored?

Memories are stored as vectors in a PostgreSQL database (Supabase recommended), configured through the `DATABASE_URL` environment variable.

How do I integrate MCP-Mem0 with Windsurf or n8n?

For Windsurf use `serverUrl` instead of `url` in the MCP configuration. For n8n, replace `localhost` with `host.docker.internal` in the SSE URL.

Comments

Basic information

Category

Memory & Knowledge

License

MIT license

Transports

stdio

Author

coleam00

More Memory & Knowledge MCP servers

RAG Documentation MCP Server

hannesrudolph

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

267

Tempreon

Tempreon

Person-owned, portable AI memory served over MCP. Your knowledge, preferences, and decisions — captured in one assistant, available in all of them.

PLUR

plur-ai

AI agents start every session with amnesia — you re-explain the project, repeat your preferences, and correct the same mistakes over and over. PLUR gives them a memory that persists. Your agent's corrections, preference

226

Anytype MCP Server

anyproto

An MCP server enabling AI assistants to interact with Anytype - your encrypted, local and collaborative wiki - to organize objects, lists, and more through natural language.

467

Memlord

MyrikLD

Memlord is a remote MCP server available at https://app.memlord.com.