Connectto Mem0 MCP Server

Seamlessly integrate your CrewAI multi-agent systems with Mem0 using Klavis AI's comprehensive MCP server connection guide.

Mem0 icon

Mem0

featured

Mem0 is an intelligent memory layer for AI applications that provides long-term memory storage and retrieval. Store code snippets, implementation details, and programming knowledge for seamless context retention across conversations

Available Tools:

  • mem0_add_memory
  • mem0_get_all_memories
  • mem0_search_memories
  • +2 more tools

Quick Setup Guide

Follow these steps to connect CrewAI to this MCP server

1

Create Your Account

Sign up for KlavisAI to access our MCP server management platform.

2

Configure Agents & Tools

Set up your CrewAI agents with the MCP server tools and configure authentication settings for collaborative workflows.

3

Deploy Your Crew

Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.

CrewAI + KlavisAI Integration Snippets

import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import MCPServerAdapter
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

mem0_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.MEM0,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

with MCPServerAdapter(mem0_mcp_instance.server_params) as mcp_tools:
    # Create a Mem0 Analysis Agent
    mem0_agent = Agent(
        role="Mem0 Analyst",
        goal="Research and analyze mem0 to extract comprehensive insights",
        backstory="You are an expert at analyzing mem0 and creating professional summaries.",
        tools=mcp_tools,
        reasoning=True,
        verbose=False
    )
    
    # Define Task
    analysis_task = Task(
        description=f"Research and analyze mem0 data. Extract relevant information and create a comprehensive summary with key points and main takeaways.",
        expected_output="Complete analysis with structured summary, key insights, and main takeaways",
        agent=mem0_agent,
        markdown=True
    )
    
    # Create and execute the crew
    mem0_crew = Crew(
        agents=[mem0_agent],
        tasks=[analysis_task],
        verbose=False,
        process=Process.sequential
    )
    
    result = mem0_crew.kickoff()

Frequently Asked Questions

Everything you need to know about connecting CrewAI to this MCP server

Ready to Get Started?

Join developers who are already using KlavisAI to power their CrewAI multi-agent systems with this MCP server.

Start For Free