Enhance your LlamaIndex 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 LlamaIndex agents
You’ll need valid Membit API credentials. If you don’t have access yet, get your API key to get started.

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Installation

Install the required LlamaIndex MCP tools package: 
pip install llama-index-tools-mcp
We recommend using a virtual environment to manage your dependencies and avoid conflicts.

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Quick Start

1

Import the required modules

First, import the necessary components from LlamaIndex:
from llama_index.tools.mcp import (
    BasicMCPClient,
    get_tools_from_mcp_url,
    aget_tools_from_mcp_url,
)
from llama_index.core.agent import ReActAgent
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.llms.openai import OpenAI
2

Get Membit tools from MCP server

Connect to Membit’s MCP server to retrieve available tools:
# Create MCP Client
mcp_client = BasicMCPClient(
    command_or_url="npx",
    args=[
        "mcp-remote",
        "https://mcp.membit.ai/mcp",
        "--header",
        "X-Membit-Api-Key:${MEMBIT_API_KEY}",
    ],
    env={
        "MEMBIT_API_KEY": <your-api-key>,
    },
)
# Async method
tools = await aget_tools_from_mcp_url(
    "",
    client=mcp_client,
)
Replace <your-api-key> with your actual Membit API key. Keep this credential secure and don’t share it with unauthorized users.
3

Create and configure your agent

Set up a LlamaIndex ReAct agent with Membit tools:
# Initialize your LLM
llm = OpenAI(model="gpt-4o-mini")

# Create the agent with Membit tools
agent = ReActAgent(
    tools=tools,
    llm=llm,
    memory=ChatMemoryBuffer.from_defaults(),
    verbose=True,
)
Your agent is now ready to access real-time social media context through Membit!
4

Query your agent

Start asking questions that leverage real-time social data:
response = agent.query(
    "Use cluster_search to find trending Bitcoin discussions today, "
    "then use cluster_info to get details about the most interesting cluster."
)

print(response)

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Complete Example

Here’s a full working example that demonstrates the integration: 
import asyncio

from llama_index.core.agent import ReActAgent
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import aget_tools_from_mcp_url

async def main(): # Create MCP client
mcp_client = BasicMCPClient(
command_or_url="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>,
},
)

    # Get Membit tools
    tools = await aget_tools_from_mcp_url(
        "",
        client=mcp_client,
    )

    # Initialize LLM
    llm = OpenAI(model="gpt-4o-mini")

    # Create agent
    agent = ReActAgent(
        tools=tools,
        llm=llm,
        memory=ChatMemoryBuffer.from_defaults(),
        verbose=True,
    )

    # Query for trending crypto discussions
    response = await agent.aquery(
        "What are the most trending discussions about Bitcoin today? "
        "Use cluster_search to find the hottest topics and give me insights."
    )

    print(response)

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

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Troubleshooting