await phone.serve(agent, port=8000, recording=True)

from twilio.rest import Client

client = Client(account_sid, auth_token)
recordings = client.recordings.list(limit=20)
for record in recordings:
    print(record.sid, record.duration)

await phone.serve(agent, local_recording=True)

# Or pass a directory string to choose where the WAVs go:
await phone.serve(agent, local_recording="/var/recordings")

async def on_call_end(event):
    if event.get("recording_path"):
        print(f"Recording saved: {event['recording_path']}")

# AMD is on by default; just pass a voicemail_message to leave a message
# when a machine answers.
await phone.call(
    to="+15550009876",
    agent=agent,
    voicemail_message="Hi, this is Acme Corp calling about your appointment. Please call us back at 555-000-1234.",
)

# Or opt out explicitly for a known-human destination
await phone.call(
    to="+15550009876",
    agent=agent,
    machine_detection=False,
)

await phone.serve(
    agent,
    port=8000,
    voicemail_message="Hi, please call us back at your earliest convenience.",
)

User presses: 1
Agent receives: "[DTMF: 1]"

User presses: #
Agent receives: "[DTMF: #]"

agent = phone.agent(
    system_prompt="""You are an automated phone menu.
When the caller presses 1, transfer to sales.
When the caller presses 2, transfer to support.
When the caller presses 0, transfer to a human operator.""",
)

agent = phone.agent(
    system_prompt="""You are a front desk receptionist.
If the caller asks about billing, transfer them to +15550001111.
If the caller asks about technical support, transfer them to +15550002222.
If the caller asks to speak to a manager, transfer them to +15550003333.""",
)

agent = phone.agent(
    system_prompt="...",
    barge_in_threshold_ms=0,  # Disable barge-in (exact interruption)
)

from getpatter import DeepgramSTT, AnthropicLLM, ElevenLabsTTS

agent = phone.agent(
    stt=DeepgramSTT(),
    llm=AnthropicLLM(),
    tts=ElevenLabsTTS(voice_id="rachel"),
    system_prompt="You are a helpful assistant.",
    echo_cancellation=True,
)

from getpatter.audio.aec import NlmsEchoCanceller

# 8 kHz callers benefit from a longer filter window
aec = NlmsEchoCanceller(sample_rate=8000, filter_taps=1024)

agent = phone.agent(
    stt=DeepgramSTT(),
    llm=AnthropicLLM(),
    tts=ElevenLabsTTS(voice_id="rachel"),
    system_prompt="You are a helpful assistant.",
    high_pass_hz=100,   # strip DC / mains hum / rumble below 100 Hz
    agc=True,           # normalise caller level toward the target RMS
)

from getpatter import AgcConfig

agent = phone.agent(
    # ...
    agc=AgcConfig(
        target_rms_dbfs=-18.0,   # desired output level
        max_gain_db=30.0,        # never amplify the noise floor by more than this
        speech_floor_dbfs=-45.0, # frames below this are treated as non-speech
        attack_ms=10.0,          # fast gain reduction when the talker gets loud
        release_ms=200.0,        # slow gain increase when the talker gets quiet
        limiter_ceiling=0.99,    # peak ceiling as a fraction of full scale
    ),
)

agent = phone.agent(
    stt=DeepgramSTT(),
    llm=AnthropicLLM(),
    tts=ElevenLabsTTS(voice_id="rachel"),
    system_prompt="You are a helpful assistant.",
    aggressive_first_flush=True,
)

from getpatter import SentenceChunker

chunker = SentenceChunker(language="it")    # uses Italian honorifics + terminators

agent = phone.agent(
    system_prompt="...",  # shipped to the LLM verbatim
    disable_phone_preamble=True,
)

agent = phone.agent(
    system_prompt="You are a helpful assistant.",
    first_message="Hi, this is an AI-powered assistant calling on behalf of Acme Corp. How can I help you?",
)

async def on_transcript(event):
    history = event.get("history", [])
    for entry in history:
        print(f"[{entry['timestamp']}] {entry['role']}: {entry['text']}")

{
    "role": "user",       # or "assistant"
    "text": "Hello!",
    "timestamp": 1710489601.234  # Unix float from time.time()
}

import os
import asyncio
from dotenv import load_dotenv
from getpatter import Patter, Twilio, OpenAIRealtime

load_dotenv()

phone = Patter(
    carrier=Twilio(),                               # TWILIO_* from env
    phone_number=os.environ["PHONE_NUMBER"],
    webhook_url=os.environ["WEBHOOK_URL"],
)

agent = phone.agent(
    engine=OpenAIRealtime(voice="nova"),            # OPENAI_API_KEY from env
    system_prompt="""You are an appointment reminder bot for Dr. Smith's office.

Behavior:
- Confirm the patient's identity by name and date of birth.
- Remind them of their upcoming appointment.
- If they want to reschedule, transfer to the front desk at +15550001111.
- If they press 1, confirm the appointment.
- If they press 2, cancel the appointment.

Be concise and professional.""",
    first_message="Hello! This is Dr. Smith's office calling with an appointment reminder.",
    variables={
        "patient_name": "Jane Doe",
        "appointment_date": "March 20th at 2:00 PM",
    },
)

async def on_call_start(event):
    print(f"Calling {event['callee']} for appointment reminder")

async def on_call_end(event):
    transcript = event["transcript"]
    # Save transcript to your database
    print(f"Call complete. {len(transcript)} messages exchanged.")

async def main():
    await phone.serve(
        agent,
        port=8000,
        recording=True,
        on_call_start=on_call_start,
        on_call_end=on_call_end,
    )

asyncio.run(main())