Connectto Klavis ReportGen, Mem0, Google Jobs MCP Servers

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

Klavis ReportGen icon

Klavis ReportGen

featured

Generate visually appealing JavaScript web reports from search queries with Klavis AI.

Available Tools:

  • generate_web_reports
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
Google Jobs icon

Google Jobs

featured

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

Available Tools:

  • google_jobs_search
  • google_jobs_get_details
  • google_jobs_search_by_company
  • +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"))

klavis_reportgen_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.KLAVIS_REPORTGEN,
    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
klavis_reportgen_tools = MCPServerAdapter(klavis_reportgen_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
klavis_reportgen_agent = Agent(
    role="Klavis ReportGen Specialist",
    goal="Handle all Klavis ReportGen related tasks and data processing",
    backstory="You are an expert in Klavis ReportGen operations and data analysis",
    tools=klavis_reportgen_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=klavis_reportgen_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=[klavis_reportgen_agent, mem0_agent, google_jobs_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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