Connectto Mixpanel, Shopify, Mem0 MCP Servers

Create powerful collaborative AI workflows by connecting multiple MCP servers including Mixpanel, Shopify, Mem0 for enhanced multi-agent automation capabilities in Klavis AI.

Mixpanel icon

Mixpanel

featured

Mixpanel is a powerful product analytics platform that helps teams understand user behavior, track events, analyze conversion funnels, measure retention, and make data-driven decisions with real-time insights and advanced segmentation capabilities

Available Tools:

  • mixpanel_send_events
  • mixpanel_get_events
  • mixpanel_get_event_properties
  • +6 more tools
Shopify icon

Shopify

featured

Shopify is a complete commerce platform that lets you start, grow, and manage a business. Manage products, process orders, track customers, and build your online store with powerful e-commerce tools

Available Tools:

  • shopify_list_products
  • shopify_get_product
  • shopify_create_product
  • +6 more tools
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 these MCP servers

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 your desired MCP servers 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"))

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

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

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,
)

# Initialize MCP tools for each server
mixpanel_tools = MCPServerAdapter(mixpanel_mcp_instance.server_params)
shopify_tools = MCPServerAdapter(shopify_mcp_instance.server_params)
mem0_tools = MCPServerAdapter(mem0_mcp_instance.server_params)

# Create specialized agents for each service
mixpanel_agent = Agent(
    role="Mixpanel Specialist",
    goal="Handle all Mixpanel related tasks and data processing",
    backstory="You are an expert in Mixpanel operations and data analysis",
    tools=mixpanel_tools,
    reasoning=True,
    verbose=False
)

shopify_agent = Agent(
    role="Shopify Specialist",
    goal="Handle all Shopify related tasks and data processing",
    backstory="You are an expert in Shopify operations and data analysis",
    tools=shopify_tools,
    reasoning=True,
    verbose=False
)

mem0_agent = Agent(
    role="Mem0 Specialist",
    goal="Handle all Mem0 related tasks and data processing",
    backstory="You are an expert in Mem0 operations and data analysis",
    tools=mem0_tools,
    reasoning=True,
    verbose=False
)

# Define collaborative tasks
research_task = Task(
    description="Gather comprehensive data from all available sources",
    expected_output="Raw data and initial findings from all services",
    agent=mixpanel_agent,
    markdown=True
)

analysis_task = Task(
    description="Analyze and synthesize the gathered data",
    expected_output="Comprehensive analysis with insights and recommendations",
    agent=shopify_agent,
    markdown=True
)

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[mixpanel_agent, shopify_agent, mem0_agent],
    tasks=[research_task, analysis_task],
    verbose=False,
    process=Process.sequential
)

result = multi_agent_crew.kickoff()

Frequently Asked Questions

Everything you need to know about connecting CrewAI to these MCP servers

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