Connectto Resend, Tavily, Mem0 MCP Servers

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

Resend icon

Resend

featured

Resend is a modern email API for sending and receiving emails programmatically

Available Tools:

  • resend_send_email
  • resend_create_audience
  • resend_get_audience
  • +12 more tools
Tavily icon

Tavily

featured

Tavily is an AI-powered search API designed for LLMs and AI agents. Get real-time web search results, extract content from URLs, crawl websites, and generate site maps with advanced filtering and parsing capabilities

Available Tools:

  • tavily_search
  • tavily_extract
  • tavily_crawl
  • +1 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"))

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

tavily_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.TAVILY,
    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
resend_tools = MCPServerAdapter(resend_mcp_instance.server_params)
tavily_tools = MCPServerAdapter(tavily_mcp_instance.server_params)
mem0_tools = MCPServerAdapter(mem0_mcp_instance.server_params)

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

tavily_agent = Agent(
    role="Tavily Specialist",
    goal="Handle all Tavily related tasks and data processing",
    backstory="You are an expert in Tavily operations and data analysis",
    tools=tavily_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=resend_agent,
    markdown=True
)

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

# Create multi-agent crew
multi_agent_crew = Crew(
    agents=[resend_agent, tavily_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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