Integrate Orita with your
agent framework

Use Orita as a tool inside OpenAI's Agents SDK to let your agent book appointments autonomously.

from orita import OritaClient
from agents import Agent, tool

client = OritaClient(api_key="orita_xxx")

@tool
def book_appointment(date: str, time: str, client_email: str) -> dict:
    return client.book(event_type_id="...", date=date, time=time, ...)

Wire Orita into a LangGraph workflow as a LangChain tool — check availability and book in one graph.

from orita import OritaClient
from langchain_core.tools import tool

@tool
def check_availability(provider_id: str, date: str) -> str:
    slots = client.slots("evt_xxx", date, provider_id)
    return f"Available: {', '.join(s['label'] for s in slots)}"

Create a scheduling crew where agents check slots and book appointments via Orita's API.

from orita import OritaClient
from crewai.tools import BaseTool

class OritaSchedulingTool(BaseTool):
    name: str = "Schedule Appointment"
    def _run(self, provider_id: str, date: str, time: str) -> str:
        booking = client.book(...)
        return f"Booked: {booking['id']}"

Register Orita as a Mastra tool and expose scheduling capabilities to your TypeScript agents.

import { OritaClient } from 'orita-sdk'
import { createTool } from '@mastra/core/tools'

const client = new OritaClient({ apiKey: process.env.ORITA_API_KEY })

const bookAppointment = createTool({
  id: 'book-appointment',
  execute: async ({ date, time }) => client.book({ ... })
})

Build no-code scheduling automation workflows using n8n's HTTP Request nodes and Orita's REST API.

Trigger (Webhook)
→ HTTP Request (GET /professionals)
→ HTTP Request (GET /slots)
→ HTTP Request (POST /bookings)
→ Respond to Webhook