Enhance your CrewAI applications with real-time social media context from Membit. This integration allows your AI agents to access up-to-the-minute discussions, trends, and insights from platforms like X (Twitter), Farcaster, and more.

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Prerequisites

Before you begin, ensure you have:
  • Python 3.10 or higher installed
  • A Membit account with API access
  • Basic familiarity with CrewAI agents and crews
  • Node.js installed (for MCP remote client)
You’ll need valid Membit API credentials and the MCP remote URL. If you don’t have access yet, get your API key to get started.

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Installation

Install the required packages for CrewAI and MCP integration: 
# Install CrewAI tools
pip install 'crewai-tools[mcp]'

# Install MCP remote client (requires Node.js)
npm install -g mcp-remote
We recommend using a virtual environment to manage your Python dependencies and avoid conflicts.

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Quick Start

1

Import required modules

Import the necessary components for CrewAI and MCP integration:
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import StdioServerParameters
2

Configure MCP server parameters

Set up the connection to Membit’s MCP server:
server_params = StdioServerParameters(
    command="npx",
    args=[
        "mcp-remote",
        "https://mcp.membit.ai/mcp",
        "--header",
        "X-Membit-Api-Key:${MEMBIT_API_KEY}",
    ],
    env={
        "MEMBIT_API_KEY": <your-api-key>,
    },
)
Make sure you have mcp-remote installed globally via npm for this to work.
Replace <your-api-key> with your actual Membit API key. Keep this credential secure and don’t share it with unauthorized users.
3

Create your Membit-powered agent

Use the MCPServerAdapter to connect Membit tools to your CrewAI agent:
with MCPServerAdapter(server_params) as membit_tools:
    # Create an agent with Membit tools
    analyst_agent = Agent(
        role="Social Media Data Analyst",
        goal="Analyze trending social media data and provide insights using Membit tools",
        backstory="You are an experienced social media analyst with expertise in identifying trends and understanding online conversations.",
        allow_code_execution=False,
        tools=membit_tools,
    )
4

Define your analysis task

Create a task that leverages Membit’s real-time data:
    analysis_task = Task(
        description="Find the most trending discussions today about AI technology. Use cluster_search to identify hot topics and provide insights with source URLs.",
        agent=analyst_agent,
        expected_output="A comprehensive analysis of trending AI discussions with key insights and source URLs",
    )
5

Execute your crew

Create and run your CrewAI crew:
    # Create and execute the crew
    crew = Crew(
        agents=[analyst_agent],
        tasks=[analysis_task]
    )

    result = crew.kickoff()
    print(result)
Your CrewAI agent is now powered by real-time social media context from Membit!

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Complete Example

Here’s a full working example that demonstrates the integration: 
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import StdioServerParameters

def main():
server_params = StdioServerParameters(
command="npx",
args=[
"mcp-remote",
"https://mcp.membit.ai/mcp",
"--header",
"X-Membit-Api-Key:${MEMBIT_API_KEY}",
],
env={ # Replace `<your-api-key>` with your actual Membit API key.
"MEMBIT_API_KEY": <your-api-key>,
},
)

    with MCPServerAdapter(server_params) as membit_tools:
        # Create a social media analyst agent
        analyst_agent = Agent(
            role="Social Media Data Analyst",
            goal="Analyze trending social media data and provide actionable insights",
            backstory="You are an expert social media analyst who specializes in identifying trends, analyzing sentiment, and understanding online conversations across platforms.",
            allow_code_execution=False,
            tools=membit_tools,
        )

        # Create analysis task
        analysis_task = Task(
            description="What are the most trending discussions today related to Bitcoin? Use cluster_search to find hot topics and provide the best conversations to follow with URLs.",
            agent=analyst_agent,
            expected_output="A detailed analysis of trending Bitcoin discussions with source URLs and key insights",
        )

        # Execute the crew
        crew = Crew(agents=[analyst_agent], tasks=[analysis_task])
        result = crew.kickoff()

        print("Analysis Results:")
        print(result)

if **name** == "**main**":
main()

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Troubleshooting