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Documentation Index

Fetch the complete documentation index at: https://docs.getovra.com/llms.txt

Use this file to discover all available pages before exploring further.

LangGraph is graph-based agent orchestration with first-class checkpointing. Ovra’s MCP tools plug in as native LangChain tools via langchain-mcp-adapters — your nodes can declare intents, mint credentials, and pay without leaving the graph.
Sandbox-only today. Use a sk_sandbox_* or sk_test_* key.

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Install

pip install langchain-mcp-adapters langgraph langchain-openai

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Setup

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()  # all 19 ovra_* tools as LangChain Tool objects
    agent = create_react_agent(llm, tools)

    result = await agent.ainvoke({
        "messages": [
            {"role": "user", "content": "Buy a USB-C hub under EUR 30 on amazon.de"}
        ]
    })

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Human-in-the-loop with checkpointing

LangGraph’s checkpointer pairs naturally with Ovra’s enforcementLevel: "approve" policy mode — when an intent lands in pending_approval, the graph pauses; your approval surface flips it to approved; the graph resumes. 
from langgraph.checkpoint.memory import MemorySaver

agent = create_react_agent(llm, tools, checkpointer=MemorySaver())
For production, swap MemorySaver for PostgresSaver so the graph survives restarts.

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Multi-step example

[node: research] -> find best USB-C hub under EUR 30 on amazon.de
[node: declare ] -> ovra_intent { action: "declare", ... }
[node: gate    ] -> wait for 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", ... }

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Why LangGraph

  • Stateful workflows — checkpointing for multi-step payment flows
  • Human-in-the-loop built in — pairs with Ovra’s approve enforcement level
  • Multi-agent — purchaser, auditor, researcher in one graph
  • Streaming + async for long-running flows
ToolPurpose
ovra_payFull flow in one call
ovra_intentDeclare, verify, gate on approval status
ovra_credentialFine-grained lifecycle
ovra_transactionHistory + memos
ovra_policyRead before declaring
ovra_outcomeReport success for policy learning

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Next

MCP overview

Architecture and the full 19-tool list.

OpenAI Agents

Hosted MCP integration.

CrewAI

Role-based multi-agent teams.