Seamlessly integrate your CrewAI multi-agent systems with Exa using Klavis AI's comprehensive MCP server connection guide.
Exa is an AI-powered search engine designed for AI applications. Use neural search to understand meaning and context, find similar content, get direct answers with citations, and conduct comprehensive research with structured analysis
Follow these steps to connect CrewAI to this MCP server
Sign up for KlavisAI to access our MCP server management platform.
Set up your CrewAI agents with the MCP server 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"))
exa_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.EXA,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
with MCPServerAdapter(exa_mcp_instance.server_params) as mcp_tools:
# Create a Exa Analysis Agent
exa_agent = Agent(
role="Exa Analyst",
goal="Research and analyze exa to extract comprehensive insights",
backstory="You are an expert at analyzing exa and creating professional summaries.",
tools=mcp_tools,
reasoning=True,
verbose=False
)
# Define Task
analysis_task = Task(
description=f"Research and analyze exa data. Extract relevant information and create a comprehensive summary with key points and main takeaways.",
expected_output="Complete analysis with structured summary, key insights, and main takeaways",
agent=exa_agent,
markdown=True
)
# Create and execute the crew
exa_crew = Crew(
agents=[exa_agent],
tasks=[analysis_task],
verbose=False,
process=Process.sequential
)
result = exa_crew.kickoff()
Everything you need to know about connecting CrewAI to this MCP server
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