langchain-tokportal

pip install langchain-tokportal
# optional, for the LangGraph example below
pip install langchain langchain-openai

export TOKPORTAL_API_KEY=sk_...

from langchain_tokportal import TokPortalToolkit

toolkit = TokPortalToolkit.from_api_key()          # reads TOKPORTAL_API_KEY
tools = toolkit.get_tools()

for tool in tools:
    print(tool.name)
# tokportal_get_credit_balance
# tokportal_create_bundle
# tokportal_configure_bundle_video
# tokportal_publish_bundle
# tokportal_get_bundle
# tokportal_list_accounts

print(tools[0].invoke({}))                          # -> {"data": {"balance": ...}}

import asyncio

from langchain_openai import ChatOpenAI
from langchain.agents import create_agent

from langchain_tokportal import TokPortalToolkit

SYSTEM = (
    "You operate TokPortal, a managed social infrastructure API. "
    "Always check the credit balance before creating bundles. "
    "A bundle must be published for a human account manager to start working on it."
)


async def main() -> None:
    toolkit = TokPortalToolkit.from_api_key()

    # "native" = 6 SDK tools, "mcp" = full remote MCP tool set, "all" = both
    tools = await toolkit.aget_tools("mcp")

    agent = create_agent(ChatOpenAI(model="gpt-4.1"), tools, system_prompt=SYSTEM)

    result = await agent.ainvoke(
        {
            "messages": [
                (
                    "user",
                    "Order one new TikTok account in the US with 3 video slots and "
                    "advanced warming on the terms 'home workout', 'protein snacks', "
                    "'gym motivation'. Publish it and tell me the bundle id.",
                )
            ]
        }
    )
    print(result["messages"][-1].content)


asyncio.run(main())

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_tokportal import mcp_connection

client = MultiServerMCPClient({"tokportal": mcp_connection("sk_...")})
tools = await client.get_tools()