MCP Integration - Mem0

Model Context Protocol (MCP) provides a standardized way for AI agents to manage their own memory through Mem0, without manual API calls.

Why use MCP

When building AI applications, memory management often requires manual integration. MCP eliminates this complexity by:

Universal compatibility: Works with any MCP-compatible client (Claude, Claude Code, Cursor, Windsurf, VS Code, OpenCode)
Agent autonomy: AI agents decide when to save, search, or update memories
Zero infrastructure: No servers to maintain - Mem0's cloud MCP handles everything
Standardized protocol: One integration works across all your AI tools

Setup

Add Mem0 MCP to all supported clients with a single command:

bash

npx mcp-add \
  --name mem0-mcp \
  --type http \
  --url "https://mcp.mem0.ai/mcp" \
  --clients "claude,claude code,cursor,windsurf,vscode,opencode"

Or configure a specific client:

bash

npx mcp-add \
  --name mem0-mcp \
  --type http \
  --url "https://mcp.mem0.ai/mcp" \
  --clients "cursor"

For manual configuration, add this to your MCP client config:

json

{
  "mcpServers": {
    "mem0-mcp": {
      "type": "http",
      "url": "https://mcp.mem0.ai/mcp"
    }
  }
}

For detailed per-client instructions, see the Mem0 MCP Quickstart.

Available tools

The MCP server exposes 9 memory tools to your AI client:

Tool	Purpose
`add_memory`	Store conversations or facts
`search_memories`	Find relevant memories with filters
`get_memories`	List memories with pagination
`update_memory`	Modify existing memory content
`delete_memory`	Remove specific memories
`delete_all_memories`	Bulk delete memories
`delete_entities`	Remove user/agent/app entities
`get_memory`	Retrieve single memory by ID
`list_entities`	View stored entities

How it works

Configure the MCP server - Add Mem0 MCP to your AI client using the setup command above
Agent connects - Your AI client connects to Mem0's cloud MCP server over HTTP
Autonomous memory - The agent decides when to store/retrieve memories as part of its reasoning
No manual API calls - The agent manages memory automatically through MCP tools

Example interactions

Once connected, your AI agent can:

User: Remember that I'm allergic to peanuts
Agent: [calls add_memory] Got it! I've saved your peanut allergy.

User: What dietary restrictions do I know about?
Agent: [calls search_memories] You have a peanut allergy.

The agent automatically decides when to use memory tools based on context.

Try these prompts

python

# Multi-task operations
"Generate 5 user personas for our e-commerce app with different demographics, store them all, then search for existing personas"

# Natural context retrieval
"Anything about my work preferences I should remember?"

# Complex information updates
"Update my current project: the mobile app is now 80% complete, we've fixed the login issues, and the launch date is March 15"

# Time-based queries
"What meetings did I have last week about Project Phoenix?"

# Memory cleanup
"Delete all test data and temporary memories from our development phase"

# Personal preferences
"I drink oat milk cappuccino with one sugar every morning, and I prefer standing desks"

# Health and wellness tracking
"I'm allergic to peanuts and shellfish, and I go for 5km runs on weekday mornings"

These examples demonstrate how MCP enables natural language memory operations - the AI agent automatically determines when to add, search, update, or delete memories based on context.

What you can do

The Mem0 MCP server enables powerful memory capabilities for your AI applications:

Health tracking: "I'm allergic to peanuts and shellfish" - Add new health information
Research data: "Store these trial parameters: 200 participants, double-blind, placebo-controlled" - Save structured data
Preference queries: "What do you know about my dietary preferences?" - Search and retrieve relevant memories
Project updates: "Update my project status: the mobile app is now 80% complete" - Modify existing memory
Data cleanup: "Delete all memories from 2023" - Bulk remove outdated information
Topic overview: "Show me everything about Project Phoenix" - List all memories for a subject

Performance tips

Enable graph memories for relationship-aware recall
Use specific filters when searching large memory sets
Batch operations when adding multiple memories
Monitor memory usage in the Mem0 dashboard

Best practices

Use the cloud MCP: The hosted MCP server at https://mcp.mem0.ai/mcp handles infrastructure for you
Use wildcards: user_id: "*" to search across all users
Monitor usage: Track memory operations in the dashboard
Document patterns: Share successful prompt patterns with your team