Seamlessly integrate your CrewAI multi-agent systems with Mem0 using Klavis AI's comprehensive MCP server connection guide.
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 this MCP server
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Set up your CrewAI agents with the MCP server tools and configure authentication settings for collaborative workflows.
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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"))
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,
)
with MCPServerAdapter(mem0_mcp_instance.server_params) as mcp_tools:
# Create a Mem0 Analysis Agent
mem0_agent = Agent(
role="Mem0 Analyst",
goal="Research and analyze mem0 to extract comprehensive insights",
backstory="You are an expert at analyzing mem0 and creating professional summaries.",
tools=mcp_tools,
reasoning=True,
verbose=False
)
# Define Task
analysis_task = Task(
description=f"Research and analyze mem0 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=mem0_agent,
markdown=True
)
# Create and execute the crew
mem0_crew = Crew(
agents=[mem0_agent],
tasks=[analysis_task],
verbose=False,
process=Process.sequential
)
result = mem0_crew.kickoff()
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