Create powerful collaborative AI workflows by connecting multiple MCP servers including Google Docs, Mem0, Google Jobs for enhanced multi-agent automation capabilities in Klavis AI.
Google Docs is a word processor included as part of the free, web-based Google Docs Editors suite
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
Google Jobs is a comprehensive job search platform that aggregates listings from across the web. Search for jobs by location, company, employment type, and more, with detailed information about requirements, benefits, and application processes
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"))
google_docs_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_DOCS,
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,
)
google_jobs_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_JOBS,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
google_docs_tools = MCPServerAdapter(google_docs_mcp_instance.server_params)
mem0_tools = MCPServerAdapter(mem0_mcp_instance.server_params)
google_jobs_tools = MCPServerAdapter(google_jobs_mcp_instance.server_params)
# Create specialized agents for each service
google_docs_agent = Agent(
role="Google Docs Specialist",
goal="Handle all Google Docs related tasks and data processing",
backstory="You are an expert in Google Docs operations and data analysis",
tools=google_docs_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
)
google_jobs_agent = Agent(
role="Google Jobs Specialist",
goal="Handle all Google Jobs related tasks and data processing",
backstory="You are an expert in Google Jobs operations and data analysis",
tools=google_jobs_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=google_docs_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=mem0_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[google_docs_agent, mem0_agent, google_jobs_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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