Build with the Bookbag API

// npm install @bookbag/sdk
import { Bookbag } from '@bookbag/sdk'

const bookbag = new Bookbag({ apiKey: process.env.BOOKBAG_API_KEY })

// Send a customer message to your AI agent and get a reply
const { reply, resolved, handoff } = await bookbag.chat({
  agentId: 'support-agent',
  conversationId: 'conv_123',
  message: 'Where is my order #1042?',
  customer: { email: 'sam@example.com' },
})

console.log(reply)       // grounded answer with live order status
console.log(resolved)    // true if the agent fully resolved it
console.log(handoff)     // set when the agent escalates to a human

# pip install bookbag
import os
from bookbag import Bookbag

client = Bookbag(api_key=os.environ["BOOKBAG_API_KEY"])

resp = client.chat(
    agent_id="support-agent",
    conversation_id="conv_123",
    message="Can I return this for a different size?",
    customer={"email": "sam@example.com"},
)
print(resp.reply)

// Define a custom action the agent can call mid-conversation
{
  "name": "lookup_loyalty_points",
  "description": "Get a customer's loyalty point balance",
  "parameters": {
    "type": "object",
    "properties": { "email": { "type": "string" } },
    "required": ["email"]
  },
  "endpoint": "https://api.yourstore.com/loyalty",
  "auth": { "type": "bearer", "secretRef": "LOYALTY_API_KEY" }
}

// POST to your endpoint when a conversation is handed off
{
  "event": "conversation.handoff",
  "conversation_id": "conv_123",
  "reason": "refund_over_limit",
  "summary": "Customer requests $240 refund; exceeds $200 auto-limit.",
  "customer": { "email": "sam@example.com" },
  "transcript_url": "https://app.bookbag.ai/c/conv_123"
}