LangChain

uv add langchain langchain-mcp-adapters langchain-openai

import os
import asyncio
import base64
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

# Configure StackOne account
STACKONE_ACCOUNT_ID = "<account_id>"  # Your StackOne account ID

# Encode API key for Basic auth
auth_token = base64.b64encode(
    f"{os.getenv('STACKONE_API_KEY')}:".encode()
).decode()

async def main():
    # Connect to StackOne MCP server
    mcp_client = MultiServerMCPClient({
        "stackone": {
            "url": "https://api.stackone.com/mcp",
            "transport": "streamable_http",
            "headers": {
                "Authorization": f"Basic {auth_token}",
                "x-account-id": STACKONE_ACCOUNT_ID,
                "Content-Type": "application/json",
                "Accept": "application/json,text/event-stream",  # Required by MCP spec
                "MCP-Protocol-Version": "2025-06-18"
            }
        }
    })

    # Get StackOne tools
    tools = await mcp_client.get_tools()

    # Create agent with StackOne tools
llm = ChatOpenAI(model="gpt-5")
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful assistant with access to data from connected platforms."),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}")
    ])

    agent = create_tool_calling_agent(llm, tools, prompt)
    agent_executor = AgentExecutor(agent=agent, tools=tools)

    # Run agent
    result = agent_executor.invoke({"input": "List Salesforce accounts"})
    print(result["output"])

asyncio.run(main())

STACKONE_API_KEY=<stackone_api_key>
OPENAI_API_KEY=your_openai_key