LlamaIndex

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.

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.

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.

Quick Start

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

Get Membit tools from MCP server

Connect to Membit’s MCP server to retrieve available tools:

Async
Sync

# 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,
)

# 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>,
    },
)
# Synchronous method
tools = get_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.

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!

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)

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 BasicMCPClient, 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())

Troubleshooting

Connection Issues

Problem: Cannot connect to Membit MCP serverSolutions:

Verify your Membit MCP URL is correct
Check your network connection and firewall settings
Ensure your API credentials are valid and not expired
Try the synchronous method if async isn’t working

No Results Returned

Problem: Queries return empty or limited resultsSolutions:

Make your search terms more general or use synonyms
Check if the topic is currently being discussed on social media
Verify your search timeframe isn’t too restrictive
Try different query phrasings

Performance Issues

Problem: Slow response times or timeoutsSolutions:

Use async methods for better performance
Implement caching for frequently requested data
Optimize your query complexity
Check your internet connection stability

Get Started

API Usage

Usage with No-Code AI Agents

Usage with Python Agents

Usage with Typescript Agents

Data Hunters

Prerequisites

Installation

Quick Start

Complete Example

Troubleshooting

​Prerequisites

​Installation

​Quick Start

​Complete Example

​Troubleshooting

Prerequisites

Installation

Quick Start

Complete Example

Troubleshooting