Enhance your AutoGen 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 AutoGen agents
  • 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 AutoGen and MCP integration: 
# Install AutoGen with MCP tools
pip install autogenstudio

# 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 AutoGen and MCP integration:
import asyncio

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StdioServerParams
2

Configure MCP server parameters

Set up the connection to Membit’s MCP server:
params = StdioServerParams(
    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>,
    },
    read_timeout_seconds=60,
)
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

Initialize your model client

Set up your language model client:
model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")
4

Create your Membit-powered agent

Use McpWorkbench to connect Membit tools to your AutoGen agent:
async with McpWorkbench(server_params=params) as workbench:
    # List available tools
    tools = await workbench.list_tools()

    # Create assistant agent with Membit tools
    agent = AssistantAgent(
        "membit_assistant",
        model_client=model_client,
        workbench=workbench,
        reflect_on_tool_use=True,
        model_client_stream=True,
        system_message="You are a social media analyst with access to real-time data. Use Membit tools to analyze trending discussions and provide insights.",
    )
5

Run your agent

Execute your agent with a social media analysis task:
    await Console(
        agent.run_stream(
            task="What are the most trending discussions about AI today? Use cluster_search to find hot topics and provide insights with URLs."
        )
    )
Your AutoGen 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: 
import asyncio

from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StdioServerParams

async def main() -> None: # Configure MCP server connection
params = StdioServerParams(
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>,
},
read_timeout_seconds=60,
)

    # Initialize model client
    model_client = OpenAIChatCompletionClient(model="gpt-4o-mini")

    # Create and run agent with Membit tools
    async with McpWorkbench(server_params=params) as workbench:
        tools = await workbench.list_tools()
        print("Available Membit Tools:")
        for tool in tools:
            print(f"- {tool.name}: {tool.description}")

        agent = AssistantAgent(
            "membit_assistant",
            model_client=model_client,
            workbench=workbench,
            reflect_on_tool_use=True,
            model_client_stream=True,
            system_message="You are a helpful social media analyst. Use cluster_search to find trending topics, cluster_info for detailed analysis, and post_search for specific posts.",
        )

        await Console(
            agent.run_stream(
                task="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."
            )
        )

if **name** == "**main**":
asyncio.run(main())

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Troubleshooting