UIWorker

UIWorker extends LLMContextWorker with the ability to see and act on whatever the user is looking at. It connects an LLM to the client GUI over the RTVI UI channel: it receives the screen as accessibility snapshots, reacts to the user’s UI events, and acts on the page by sending commands back to the client. A UIWorker is the delegate side of a voice/UI split. A voice layer (the main pipeline’s LLM, or a separate LLMWorker) handles speech and hands screen-relevant work to the UIWorker. Because the worker auto-injects the latest screen state into its LLM context, the conversational voice LLM stays small and screen-unaware — keeping each LLM’s context focused and efficient.

from pipecat.workers.llm import tool
from pipecat.workers.ui import UIWorker


class MyUIWorker(UIWorker):
    @tool
    async def answer(self, params, text: str):
        await self.respond_to_job(text, tts_speak=True)
        await params.result_callback(None)


worker = MyUIWorker("ui", llm=OpenAILLMService(api_key="..."))

PipelineWorker connects a UIWorker to the client automatically when RTVI is enabled (the default) — no extra wiring. A working subclass needs only an LLM and a @tool that calls respond_to_job().

The client streams the screen as ui-snapshot messages and the worker drives it with ui-command / ui-job-group messages. See The RTVI Standard for the wire protocol and UIWorker patterns for the delegation and parallel-handling patterns end to end.

Configuration

Inherits name, llm, active, bridged, and defer_tool_frames from LLMWorker, plus:

context

LLMContext | None

default:"None"

Optional pre-built LLMContext. Seeded messages are part of the mutable history and are cleared on each keep_history=False reset; put durable instructions in the LLM’s system_instruction instead.

assistant_params

LLMAssistantAggregatorParams | None

default:"None"

Optional assistant-aggregator parameters, e.g. to enable context summarization for keep_history=True workers.

inject_events

bool

default:"True"

When True (the default), append each UI event to the context as a <ui_event> developer message. Override render_ui_event() to change the content, or set False to disable.

auto_inject_ui_state

bool

default:"True"

When True (the default), append the latest <ui_state> snapshot to the context before every inference (via the LLM’s on_before_process_frame hook). Set False to inject manually with inject_ui_state().

keep_history

bool

default:"False"

When False (the default), the context is cleared at the start of every job, so each turn sees only the current <ui_state> and query — best for the stateless-delegate role. When True, history accumulates across jobs so the LLM can resolve multi-turn references (“the next one”, “the Pro version”), at the cost of more tokens. Pair with context summarization to prune history.

prompt_guide

str | None

default:"UI_STATE_PROMPT_GUIDE"

Wire-format guide appended to the LLM’s system_instruction so it can parse the <ui_state> / <ui_event> messages. Defaults to UI_STATE_PROMPT_GUIDE; pass a string to override or None to disable. Living in system_instruction, it survives context resets.

Properties

Inherits all properties from LLMContextWorker (including context, user_aggregator, assistant_aggregator, llm).

current_job

worker.current_job -> BusJobRequestMessage | None

The job this worker is currently processing, or None when idle. Set when a respond turn starts and cleared when the job completes. Lets @tool methods inspect the in-flight job without threading the message through every call.

UI commands

These helpers send commands to the client. They are plain methods, not LLM tools: compose them inside a custom @tool body, or use ReplyToolMixin for the standard shape. Each is a convenience wrapper around send_command with a typed payload model from pipecat.processors.frameworks.rtvi.models.

send_command

async def send_command(self, name: str, payload: Any = None) -> None

Send a named UI command to the client. Publishes a BusUICommandMessage; when RTVI is enabled, PipelineWorker translates it into an RTVIUICommandFrame on the pipeline. Client-side handlers subscribed to RTVIEvent.UICommand (or React’s useUICommandHandler) dispatch on the command name.

Parameter	Type	Default	Description
`name`	`str`		App-defined command name (e.g. `"toast"`, `"navigate"`, or any app-specific name).
`payload`	`Any`	`None`	A pydantic `BaseModel` or dataclass (converted to a dict), a `dict` (forwarded as-is), or `None` (forwarded as `{}`).

scroll_to

async def scroll_to(self, ref: str) -> None

Bring an element into view. ref is a snapshot ref (e.g. "e42") from the latest <ui_state>.

highlight

async def highlight(self, ref: str) -> None

Briefly flash an element to draw the user’s attention.

select_text

async def select_text(
    self,
    ref: str,
    *,
    start_offset: int | None = None,
    end_offset: int | None = None,
) -> None

Select an element’s text — used for deixis (pointing at content via the page’s text selection). Selects the whole element by default, or the start_offset..end_offset character sub-range when both are given.

click

async def click(self, ref: str) -> None

Click an element (checkboxes, radios, submit buttons). The standard client handler no-ops on disabled targets.

set_input_value

async def set_input_value(self, ref: str, value: str, *, replace: bool = True) -> None

Fill a text input or textarea. With replace=True (the default) the field is overwritten; with replace=False the value is appended.

Responding to jobs

A UIWorker answers via a built-in single-flight respond job. When a requester dispatches self.job("ui", name="respond", payload={"query": "..."}), the worker clears its context (unless keep_history=True), injects the current <ui_state>, appends the query as a user message, and runs one LLM turn. A @tool ends the turn by calling respond_to_job().

respond_to_job

async def respond_to_job(
    self,
    answer: str | None = None,
    *,
    tts_speak: bool = False,
    status: JobStatus = JobStatus.COMPLETED,
) -> None

Complete the in-flight job with the worker’s answer. The two delivery modes are mutually exclusive (one voice per turn):

default — the job responds with {"answer": answer} for the requester’s voice LLM to phrase.
tts_speak=True — answer is spoken verbatim by the requester’s TTS (and added to its context) while the job responds None so the voice LLM doesn’t also speak.

A falsy answer completes the turn silently — useful for the parallel-handling pattern where a separate voice layer owns speech. No-op when no job is in flight.

Parameter	Type	Default	Description
`answer`	`str \| None`	`None`	The worker’s answer — spoken verbatim or handed to the voice LLM.
`tts_speak`	`bool`	`False`	Speak `answer` verbatim via the requester’s TTS instead of returning it.
`status`	`JobStatus`	`JobStatus.COMPLETED`	Completion status.

render_query

def render_query(self, message: BusJobRequestMessage) -> str

Extract the user’s query text from a job request. The default reads payload["query"]. Override to read a different payload shape; the returned string is appended to the context as a user message before the LLM runs.

render_ui_state

def render_ui_state(self) -> str

Render the latest accessibility snapshot as a <ui_state> block (Playwright-MCP-style indented text with stable element refs). When the snapshot carries a text selection, a nested <selection ref="...">...</selection> block is appended so the LLM can resolve deictic references. Returns an empty string if no snapshot has been received. Override to customize the rendered form.

inject_ui_state

async def inject_ui_state(self) -> None

Append the latest <ui_state> block to the LLM context manually. No-op when no snapshot has been received. Use this when auto_inject_ui_state=False.

render_ui_event

def render_ui_event(self, message: BusUIEventMessage) -> str

Render a UI event as a string for context injection. The default wraps the event in a single <ui_event name="..."> tag with a JSON-encoded payload. Override to customize the injected content.

Job groups

A UIWorker can fan work out to peer workers and surface the work to the client as a cancellable progress card. These are distinct from the inherited worker-to-worker job_group (which is invisible to the client).

ui_job_group

def ui_job_group(
    self,
    *worker_names: str,
    name: str | None = None,
    payload: dict | None = None,
    timeout: float | None = None,
    cancel_on_error: bool = True,
    label: str | None = None,
    cancellable: bool = True,
) -> UIJobGroupContext

Dispatch a job group whose lifecycle is forwarded to the client as ui-job-group envelopes (group_started → job_update* → job_completed × N → group_completed). Use as an async with context manager to consume worker events inline.

Parameter	Type	Default	Description
`*worker_names`	`str`		Names of the workers to send the job to.
`name`	`str \| None`	`None`	Optional job name for routing to named `@job` handlers.
`payload`	`dict \| None`	`None`	Optional structured data describing the work.
`timeout`	`float \| None`	`None`	Optional timeout (seconds) covering both the ready-wait and execution.
`cancel_on_error`	`bool`	`True`	Whether to cancel the group if a worker errors.
`label`	`str \| None`	`None`	Human-readable label the client uses to title the in-flight card.
`cancellable`	`bool`	`True`	Whether the client may cancel the group via `ui-cancel-job-group`.

async with self.ui_job_group(
    "researcher_a", "researcher_b",
    payload={"query": query},
    label=f"Research: {query}",
) as tg:
    async for event in tg:
        ...
    results = tg.responses

start_ui_job_group

async def start_ui_job_group(self, *worker_names: str, ...) -> str

Fire-and-forget version of ui_job_group with the same parameters. Dispatches the group in the background and returns the job_id immediately (the lifecycle still forwards to the client). Use it when a @tool wants to kick off work and unblock the voice worker.

@tool
async def reply(self, params, answer, research_query=None):
    if research_query:
        await self.start_ui_job_group(
            "wikipedia", "news", "scholar",
            payload={"query": research_query},
            label=f"Research: {research_query}",
        )
    await self.respond_to_job(answer)
    await params.result_callback(None)

Handling UI events

@ui_event

from pipecat.workers.ui import ui_event

def ui_event(name: str)

Mark a worker method as a handler for a named UI event. When the client dispatches an event via PipecatClient.sendUIEvent(event, payload), the matching handler runs in its own task. The handler receives the BusUIEventMessage (read message.payload for the event data).

class MyUIWorker(UIWorker):
    @ui_event("note_click")
    async def on_note_click(self, message):
        ref = (message.payload or {}).get("ref")
        await self.scroll_to(ref)
        await self.select_text(ref)

Two handlers can’t share the same event name on the same subclass. Overrides in subclasses take precedence over base-class definitions.

ReplyToolMixin

from pipecat.workers.ui import ReplyToolMixin

ReplyToolMixin exposes a single bundled reply tool covering the full standard action set, for subclasses that don’t need a custom tool schema. Compose it ahead of UIWorker:

class MyUIWorker(ReplyToolMixin, UIWorker):
    ...

The tool requires a spoken answer (enforced by the schema, so the model can’t omit the terminator) plus optional visual and state-changing actions. It’s called exactly once per turn:

async def reply(
    self,
    params: FunctionCallParams,
    answer: str,
    scroll_to: str | None = None,
    highlight: list[str] | None = None,
    select_text: str | None = None,
    fills: list[dict] | None = None,
    click: list[str] | None = None,
)

Field	Type	Description
`answer`	`str` (required)	The spoken reply in plain language.
`scroll_to`	`str \| None`	Snapshot ref to scroll into view.
`highlight`	`list[str] \| None`	Snapshot refs to flash briefly.
`select_text`	`str \| None`	Snapshot ref to place the page’s text selection on.
`fills`	`list[dict] \| None`	`{"ref", "value"}` objects to write into inputs.
`click`	`list[str] \| None`	Snapshot refs to click in order.

Dispatch order within a turn is scroll_to → highlight → select_text → fills → click → speak the answer. The answer is delivered as verbatim TTS (respond_to_job(answer, tts_speak=True)). Apps that want a minimal schema, app-specific commands, or the requester’s voice LLM to phrase the reply should write their own @tool reply on the UIWorker subclass instead.

Pipecat Server

Client SDKs

Pipecat Flows

Pipecat Cloud

CLI

Pipecat Context Hub

Configuration

Properties

current_job

UI commands

send_command

scroll_to

highlight

select_text

click

set_input_value

Responding to jobs

respond_to_job

render_query

render_ui_state

inject_ui_state

render_ui_event

Job groups

ui_job_group

start_ui_job_group

Handling UI events

@ui_event

ReplyToolMixin

​Configuration

​Properties

​current_job

​UI commands

​send_command

​scroll_to

​highlight

​select_text

​click

​set_input_value

​Responding to jobs

​respond_to_job

​render_query

​render_ui_state

​inject_ui_state

​render_ui_event

​Job groups

​ui_job_group

​start_ui_job_group

​Handling UI events

​@ui_event

​ReplyToolMixin

Configuration

Properties

current_job

UI commands

send_command

scroll_to

highlight

select_text

click

set_input_value

Responding to jobs

respond_to_job

render_query

render_ui_state

inject_ui_state

render_ui_event

Job groups

ui_job_group

start_ui_job_group

Handling UI events

@ui_event

ReplyToolMixin