Quick Start

REST API Client

List your phone numbers, carriers, and applications:

import asyncio
from jambonz import AsyncJambonzClient

async def main():
    async with AsyncJambonzClient() as client:
        # Phone numbers
        numbers = await client.phone_numbers.list()
        for n in numbers:
            print(f"{n.number} → app: {n.application_sid}")

        # Carriers
        carriers = await client.carriers.list()
        for c in carriers:
            print(f"{c.name} ({c.voip_carrier_sid})")

        # Gateways for a carrier
        gateways = await client.gateways.list(
            voip_carrier_sid=carriers[0].voip_carrier_sid
        )
        for gw in gateways:
            print(f"  {gw.ipv4}:{gw.port}")

        # Recent calls
        calls = await client.calls.list(days=7, page=1, count=10)
        for call in calls:
            print(f"{call.from_} → {call.to} ({call.duration}s)")

asyncio.run(main())

Note

You can also use the synchronous JambonzClient if you don't need async.

Webhook Response

Build call flow instructions to respond to jambonz webhooks:

from fastapi import FastAPI
from jambonz import JambonzResponse, Say, Gather, Hangup, Input, Synthesizer

app = FastAPI()

@app.post("/incoming-call")
def handle_call() -> JambonzResponse:
    return [
        Say(
            text="Welcome! Please say something after the beep.",
            synthesizer=Synthesizer(vendor="google", language="en-US"),
        ),
        Gather(
            input=[Input.SPEECH],
            action_hook="/handle-speech",
            timeout=15,
        ),
    ]

@app.post("/handle-speech")
def handle_speech(request: Request) -> JambonzResponse:
    # Process speech result and respond
    return [
        Say(text="Thanks for calling! Goodbye."),
        Hangup(),
    ]

Using with LLM

Connect to ElevenLabs or OpenAI for conversational AI:

from jambonz import JambonzResponse, LLM, LLMAuth

@app.post("/ai-call")
def handle_ai_call() -> JambonzResponse:
    return [
        LLM(
            vendor="elevenlabs",
            auth=LLMAuth(agent_id="your-agent-id"),
            llm_options={},
            action_hook="/llm-complete",
        ),
    ]