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This tutorial explains how to integrate Memobase with the Model Context Protocol (MCP) to provide your AI agents with persistent, long-term memory. By using the Memobase MCP server, your agents can store, retrieve, and search memories, making them stateful and context-aware across conversations.

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What is MCP?

The Model Context Protocol is an open standard that allows AI assistants to securely connect to external data sources and tools. This enables them to access real-time information, execute functions, and maintain a persistent state, breaking free from the limitations of their training data.

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Why Memobase + MCP?

Traditional AI conversations are stateless. The Memobase MCP server changes this by providing:
  1. Persistent Memory: Store conversation history and user preferences across sessions.
  2. Semantic Search: Find relevant context using natural language queries.
  3. User Profiles: Build a comprehensive understanding of users over time.
  4. Cross-Platform Compatibility: Works with any MCP-compatible client, such as Claude Desktop, Cursor, or Windsurf.

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Setup

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Prerequisites

  • Python 3.11+
  • A Memobase backend (either local or cloud)

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Installation

We recommend using uv for installation: 
# Install uv if you don't have it
pip install uv

# Clone the repository
git clone https://github.com/memodb-io/memobase
cd memobase/src/mcp

# Install dependencies
uv pip install -e .

# Configure environment
cp .env.example .env
# Edit .env with your Memobase credentials

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Environment Configuration

Configure your .env file: 
TRANSPORT=sse
HOST=0.0.0.0
PORT=8050
MEMOBASE_API_KEY="your_api_key_here"
MEMOBASE_BASE_URL="https://api.memobase.dev"

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Running the MCP Server

Start the server using uv: 
uv run src/main.py
The server will be available at http://localhost:8050 with an SSE endpoint at /sse.

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Client Integration

Configure your MCP client to connect to the Memobase server. For example, in Cursor, add this to your .cursor/mcp.json: 
{
  "mcpServers": {
    "memobase": {
      "transport": "sse",
      "url": "http://localhost:8050/sse"
    }
  }
}

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Available Tools

The Memobase MCP server exposes three powerful tools to your AI agent.

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1. save_memory

Stores information in long-term memory with semantic indexing. 
# Example usage in your AI agent
await save_memory("User prefers dark mode and uses Python for backend development.")

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2. search_memories

Finds relevant context using natural language queries. 
# Search for relevant memories
context = await search_memories("What are the user's programming language preferences?")

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3. get_user_profiles

Retrieves a comprehensive, structured user profile. 
# Get the full user profile
profiles = await get_user_profiles()

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Real-World Example

Without Memory:
User: “I prefer Python for backend development.” AI: “That’s great! Python is excellent for backend work.” Later… User: “What’s the best language for my new API?” AI: “There are many options, like Python, Node.js, or Go…”
With Memobase MCP:
User: “I prefer Python for backend development.” AI: “Got it. I’ll remember your preference for Python.” (Memory saved: “User prefers Python for backend development”) Later… User: “What’s the best language for my new API?” AI: (Searches memories) “Based on your preference for Python, I’d recommend using FastAPI or Django.”

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Conclusion

The Memobase MCP server transforms stateless AI interactions into intelligent, context-aware conversations. By providing persistent memory through a standardized protocol, you can build AI applications that learn, remember, and deliver truly personalized experiences.