allow_user_dtmf

user_dtmf_options

json
"allow_user_dtmf": true,
"user_dtmf_options": {
  "digit_limit": 25.5,
  "termination_key": "#",
  "timeout_ms": 8000
}

user_dtmf_options

python
import json
import os
from dotenv import load_dotenv
from fastapi import FastAPI, Request, WebSocket

from fastapi.responses import JSONResponse, PlainTextResponse
from fastapi.websockets import WebSocketState
app = FastAPI()
@app.websocket("/llm-websocket/{call_id}")
async def websocket_handler(websocket: WebSocket, call_id: str) :
 await websocket.accept()
 # A unique call id is the identifier of each call
 print(f"Handle llm ws for: {call_id}")
 # You need to send the first message here, but for now let's skip that.
 # listen for new updates
 try:
 while True:
 message = await websocket.receive_text()
 print(message) ;
 except Exception as e:
 print(f'LLM WebSocket error for {call_id}: {e}')
 finally:
 try:
 await websocket.close()
 except RuntimeError as e:
 print(f"Websocket already closed for {call_id}")
 print(f"Closing llm ws for: {call_id}")

call_id

update_only

response_required

reminder_required

response_type

transcript

python
import json
import asyncio
from fastapi import WebSocket

# Store conversation data per call
conversation_store = {}

@app.websocket("/llm-websocket/{call_id}")
async def websocket_handler(websocket: WebSocket, call_id: str) :
    await websocket.accept()
    
    # Initialize conversation storage for this call
    conversation_store[call_id] = {
        "transcript": [],
        "tool_calls": []
    }
    
    try:
        while True:
            message = await websocket.receive_text()
            data = json.loads(message)
            
            # Extract transcript from update events
            if data.get("interaction_type") in ["update_only", "response_required", "reminder_required"]:
                if "transcript" in data:
                    conversation_store[call_id]["transcript"] = data["transcript"]
                    
                    # Save current state to file
                    with open(f"conversation_{call_id}.json", "w") as f:
                        json.dump(conversation_store[call_id], f, indent=2)
            
            # Load and apply new configuration
            new_config_path = f"new_config_{call_id}.json"
            if os.path.exists(new_config_path) :
                with open(new_config_path, "r") as f:
                    new_data = json.load(f)
                    
                # Send response with new data
                response = {
                    "response_type": "response",
                    "response_id": data.get("response_id", 0) ,
                    "content": new_data.get("content", "") ,
                    "content_complete": True
                }
                await websocket.send_text(json.dumps(response) )
                
    except Exception as e:
        print(f"Error for {call_id}: {e}")
    finally:
        # Export final conversation data
        final_export = f"final_conve

rsation_{call_id}.json"
        with open(final_export, "w") as f:
            json.dump(conversation_store[call_id], f, indent=2)
        
        await websocket.close()

python
import os
from retell import Retell

# Initialize Retell client
client = Retell(api_key="key_xxxxxx")

# Replace with your values
agent_id = "agent_xxxxxxx"

# Step 1: Get the agent and extract conversation_flow_id
agent = client.agent.retrieve(agent_id)
response_engine = agent.response_engine

if isinstance(response_engine, dict) :
    conversation_flow_id = response_engine.get("conversation_flow_id")
else:
    conversation_flow_id = getattr(response_engine, "conversation_flow_id", None)

if not conversation_flow_id:
    raise ValueError("No conversation_flow_id found on this agent.")

# Step 2: Retrieve the current conversation flow
conversation_flow_response = client.conversationFlow.retrieve(conversation_flow_id)
print(conversation_flow_response.conversation_flow_id)

python
# Step 3: Extract current flow data
current_flow = conversation_flow_response

# Step 4: Load your new JSON configuration
import json
with open("new_conversation_flow.json", "r") as f:
    new_flow_data = json.load(f)

# Step 5: Update the conversation flow with new data
updated_flow = client.conversationFlow.update(
    conversation_flow_id=conversation_flow_id,
    model_choice=new_flow_data.get("model_choice") ,
    model_temperature=new_flow_data.get("model_temperature") ,
    nodes=new_flow_data.get("nodes") ,
    start_speaker=new_flow_data.get("start_speaker") ,
    global_prompt=new_flow_data.get("global_prompt") ,
    # Add other fields as needed from your JSON
)

print(f"Updated conversation flow: {updated_flow.conversation_flow_id}")

client.conversationFlow.retrieve(conversation_flow_id)

client.conversationFlow.update()

client.conversationFlow.list()

model_choice

nodes

start_speaker

global_prompt

tools

json
"begin_after_user_silence_ms": 2000

model_choice

nodes

start_speaker

global_prompt

tools