ConnectLangChainto Postgres, Confluence, Resend MCP Servers

import os
import asyncio
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY"))

postgres_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.POSTGRES,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

confluence_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.CONFLUENCE,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

resend_mcp_instance = klavis_client.mcp_server.create_server_instance(
    server_name=McpServerName.RESEND,
    user_id="1234",
    platform_name="Klavis",
    connection_type=ConnectionType.STREAMABLE_HTTP,
)

mcp_client = MultiServerMCPClient({
    "postgres": {
        "transport": "streamable_http",
        "url": postgres_mcp_instance.server_url
    },
    "confluence": {
        "transport": "streamable_http",
        "url": confluence_mcp_instance.server_url
    },
    "resend": {
        "transport": "streamable_http",
        "url": resend_mcp_instance.server_url
    }
})

tools = asyncio.run(mcp_client.get_tools())

agent = create_react_agent(
    model=llm,
    tools=tools,
)

response = asyncio.run(agent.ainvoke({
    "messages": [{"role": "user", "content": "Your query here"}]
}))