LangGraph

pip install langchain-mcp-adapters langgraph langchain-openai

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4.1")

async with MultiServerMCPClient({
    "ovra": {
        "url": "https://api.getovra.com/api/mcp",
        "transport": "streamable_http",
        "headers": {"Authorization": "Bearer sk_sandbox_..."},
    }
}) as client:
    tools = client.get_tools()  # alle 19 ovra_* Tools als LangChain-Tool-Objekte
    agent = create_react_agent(llm, tools)

    result = await agent.ainvoke({
        "messages": [
            {"role": "user", "content": "Kauf einen USB-C Hub unter EUR 30 auf amazon.de"}
        ]
    })

from langgraph.checkpoint.memory import MemorySaver

agent = create_react_agent(llm, tools, checkpointer=MemorySaver())

[node: research] -> finde besten USB-C Hub unter EUR 30 auf amazon.de
[node: declare ] -> ovra_intent { action: "declare", ... }
[node: gate    ] -> warte auf intent.status == "approved"
[node: pay     ] -> ovra_pay { action: "checkout", ... }
[node: verify  ] -> ovra_intent { action: "verify", actualAmountEuros, actualMerchant }
[node: report  ] -> ovra_outcome { action: "report", type: "purchase_completed", ... }