Create powerful collaborative AI workflows by connecting multiple MCP servers including Freshdesk, Tavily, Mem0 for enhanced multi-agent automation capabilities in Klavis AI.
Freshdesk is a cloud-based customer support software that helps businesses manage customer inquiries across multiple channels. Create and manage tickets, contacts, companies, and agents with powerful automation and collaboration features
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
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
Follow these steps to connect CrewAI to these MCP servers
Sign up for KlavisAI to access our MCP server management platform.
Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.
Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.
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"))
freshdesk_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.FRESHDESK,
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
freshdesk_tools = MCPServerAdapter(freshdesk_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
freshdesk_agent = Agent(
role="Freshdesk Specialist",
goal="Handle all Freshdesk related tasks and data processing",
backstory="You are an expert in Freshdesk operations and data analysis",
tools=freshdesk_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=freshdesk_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=[freshdesk_agent, tavily_agent, mem0_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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