LangGraph

pip install langgraph langchain-mcp-adapters

import os

from langchain.agents import create_agent
from langchain_mcp_adapters.client import MultiServerMCPClient


async def build_agent():
    agent_id = os.environ["BEEOS_AGENT_ID"]
    client = MultiServerMCPClient(
        {
            "beeos": {
                "transport": "http",
                "url": f"https://mcp.beeos.ai/{agent_id}/mcp",
                "headers": {
                    "Authorization": f"Bearer {os.environ['BEEOS_API_KEY']}"
                },
            }
        }
    )
    tools = await client.get_tools()
    return create_agent(os.environ["LLM_MODEL"], tools)

import os
import httpx


async def create_beeos_task(state: dict) -> dict:
    headers = {"Authorization": f"Bearer {os.environ['BEEOS_API_KEY']}"}
    body = {
        "message": state["phone_task"],
        "idempotency_key": state["run_id"],
    }
    async with httpx.AsyncClient(base_url="https://openapi.beeos.ai") as client:
        response = await client.post(
            f"/api/v1/agents/{state['agent_id']}/tasks",
            headers=headers,
            json=body,
        )
        response.raise_for_status()
        return {"beeos_task": response.json()}