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The RTVIProcessor manages bidirectional communication between clients and your Pipecat application. It processes client messages, handles service configuration, executes actions, and coordinates function calls.

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Initialization

RTVIProcessor is automatically added to your pipeline when you create a PipelineTask. Access it via task.rtvi: 
pipeline = Pipeline([
    transport.input(),
    stt,
    # ... other processors ...
    transport.output()
])

task = PipelineTask(pipeline)

# Access the processor
rtvi = task.rtvi
To provide a custom processor (e.g., for Google RTVI): 
from pipecat.services.google.rtvi import GoogleRTVIProcessor

task = PipelineTask(pipeline, rtvi_processor=GoogleRTVIProcessor())
To disable RTVI entirely, set enable_rtvi=False.

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Readiness Protocol

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Client Ready State

Clients indicate readiness by sending a client-ready message, triggering the on_client_ready event in the processor: 
@rtvi.event_handler("on_client_ready")
async def on_client_ready(rtvi):
    # Handle client ready state
    await rtvi.set_bot_ready()
    # Initialize conversation
    await task.queue_frames([...])

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Bot Ready State

The server must mark the bot as ready before it can process client messages: 
await rtvi.set_bot_ready()
When marked ready, the bot sends a response containing:
  • RTVI protocol version
  • Current service configuration
  • Available actions

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Services

Services represent configurable components of your application that clients can interact with.

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Registering Services

# 1. Define option handler
async def handle_voice_option(processor, service, option):
    voice_id = option.value
    # Apply configuration change
    logger.info(f"Voice ID updated to: {voice_id}")

# 2. Create RTVIService
voice_service = RTVIService(
    name="voice",
    options=[
        RTVIServiceOption(
            name="voice_id",
            type="string",
            handler=handle_voice_option
        )
    ]
)

# 3. Register with processor
rtvi.register_service(voice_service)

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Option Types

Services support multiple data types for configuration: 
RTVIServiceOption(
    name="temperature",
    type="number",  # number, string, bool, array, object
    handler=handle_temperature
)
Option handlers receive:
  • The processor instance
  • The service name
  • The option configuration with new value

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Actions

Actions are server-side functions that clients can trigger with arguments.

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Registering Actions

# 1. Define handler function
async def handle_print_message(processor, service, arguments):
    message = arguments.get("message", "Default message")
    logger.info(f"Print action triggered with message: {message}")
    return True

# 2. Create and register RTVIAction
print_action = RTVIAction(
    service="conversation",
    action="print_message",
    arguments=[
        RTVIActionArgument(name="message", type="string")
    ],
    result="bool",
    handler=handle_print_message
)
rtvi.register_action(print_action)

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Action Arguments

Actions can accept typed arguments from clients: 
search_action = RTVIAction(
    service="knowledge",
    action="search",
    arguments=[
        RTVIActionArgument(name="query", type="string"),
        RTVIActionArgument(name="limit", type="number")
    ],
    result="array",
    handler=handle_search
)

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Function Calls

Handle LLM function calls with client interaction: 

await processor.handle_function_call(
    function_name=function_name,
    tool_call_id=tool_call_id,
    arguments=arguments,
)

await processor.handle_function_call(params)
The function call process:
  1. LLM requests a function call
  2. Processor notifies client with llm-function-call message
  3. Client executes function and returns result
  4. Result is passed back to LLM via FunctionCallResultFrame
  5. Conversation continues

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Error Handling

Send error messages to clients: 
# General error
await processor.send_error("Invalid configuration")

# Request-specific error
await processor._send_error_response(request_id, "Invalid action arguments")
Error categories:
  • Configuration errors
  • Action execution errors
  • Function call errors
  • Protocol errors
  • Fatal and non-fatal errors

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Bot Control

Manage bot state and handle interruptions: 
# Set bot as ready
await processor.set_bot_ready()

# Handle interruptions
await processor.interrupt_bot()

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Custom Messaging

Send custom messages from server to client: 
from pipecat.processors.frameworks.rtvi import RTVIServerMessageFrame

# Send a custom message
frame = RTVIServerMessageFrame(
    data={
        "type": "custom-event",
        "payload": {"key": "value"}
    }
)
await rtvi.push_frame(frame)
The client receives these messages through the onServerMessage callback or serverMessage event.