Enhance your LangChain 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 LangChain 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 LangChain and MCP integration: 
# Install LangChain MCP adapter
pip install langchain-mcp-adapters

# 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 LangChain and MCP integration:
import asyncio

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from langchain.agents import create_openai_tools_agent, AgentExecutor
from langchain.chat_models import init_chat_model
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.schema import HumanMessage
from langchain_mcp_adapters.tools import load_mcp_tools
2

Configure your LLM

Set up your language model with the provider of your choice:
llm = init_chat_model(
    model="gpt-4o",
    model_provider="openai",
)
3

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.
4

Create agent prompt

Define a prompt template that encourages the agent to use Membit tools:
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "Make sure you utilize membit tools to get the most trending data."
        ),
        MessagesPlaceholder(variable_name="messages"),
        MessagesPlaceholder(variable_name="agent_scratchpad"),
    ]
)
5

Create and run your agent

Connect to Membit and create your LangChain agent:
async def main():
    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            # Initialize the MCP connection
            await session.initialize()

            # Load Membit tools from MCP session
            mcp_tools = await load_mcp_tools(session)

            # Create the LangChain agent
            agent = create_openai_tools_agent(
                llm=llm,
                tools=mcp_tools,
                prompt=prompt
            )

            # Create executor
            agent_executor = AgentExecutor(
                agent=agent,
                tools=mcp_tools,
                verbose=True
            )

            # Query your agent
            await agent_executor.ainvoke({
                "messages": [HumanMessage(content=
                    "What are the most trending discussions about AI today?"
                )]
            })

if __name__ == "__main__":
    asyncio.run(main())
Your LangChain agent is now powered by real-time social media context from Membit!

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Complete Example

Here’s a full working example with enhanced functionality: 
import asyncio

from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.chat_models import init_chat_model
from langchain.schema import HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_mcp_adapters.tools import load_mcp_tools
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Initialize LLM

llm = init_chat_model(
model="gpt-4o",
model_provider="openai",
)

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>,
},
)

prompt = ChatPromptTemplate.from_messages([
(
"system",
"You are a social media analyst with access to real-time data. "
"Make sure you utilize membit tools to get the most trending data."
),
MessagesPlaceholder(variable_name="messages"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
])

async def main():
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()

            mcp_tools = await load_mcp_tools(session)

            agent = create_openai_tools_agent(
                llm=llm,
                tools=mcp_tools,
                prompt=prompt
            )

            agent_executor = AgentExecutor(
                agent=agent,
                tools=mcp_tools,
                verbose=True
            )

            user_input = (
                "What are the most trending discussions today related to Bitcoin? "
                "Give me the best conversations to follow with URLs."
            )

            await agent_executor.ainvoke({
                "messages": [HumanMessage(content=user_input)]
            })


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

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Troubleshooting