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

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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

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Setup

Add Mem0 MCP to all supported clients with a single command: 
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: 
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: 
{
  "mcpServers": {
    "mem0-mcp": {
      "type": "http",
      "url": "https://mcp.mem0.ai/mcp"
    }
  }
}
For detailed per-client instructions, see the Mem0 MCP Quickstart.

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Available tools

The MCP server exposes 9 memory tools to your AI client:
ToolPurpose
add_memoryStore conversations or facts
search_memoriesFind relevant memories with filters
get_memoriesList memories with pagination
update_memoryModify existing memory content
delete_memoryRemove specific memories
delete_all_memoriesBulk delete memories
delete_entitiesRemove user/agent/app entities
get_memoryRetrieve single memory by ID
list_entitiesView stored entities

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How it works

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

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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.

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Try these prompts

# 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.

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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

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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

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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

Memory Filters

Gemini 3 with MCP