pipecat/examples/multi-worker/ui-worker/async-tasks

async-tasks

The UIWorker fans out long-running work to multiple peer workers in parallel, streams their progress to an in-flight panel on the page, and lets the user cancel mid-flight.

What it shows

• The user_job_group / start_user_job_group API: dispatching parallel work to multiple peer workers and automatically forwarding every job lifecycle event to the client. The reply tool calls start_user_job_group("wikipedia", "news", "scholar", payload=..., label=...) and the UIWorker does the rest.
• The four ui-task envelopes the worker forwards (group_started, task_update, task_completed, group_completed) and the client-side RTVIEvent.UITask event for consuming them. The client keeps a state map keyed by task_id and renders per-worker progress.
• Cancellation: the in-flight card's Cancel button calls client.cancelUITask(task_id, reason). The reserved __cancel_task event is translated by the UIWorker into cancel_job_group(task_id) on the registered group; cancelled workers report status cancelled.
• Background dispatch from a tool: start_user_job_group returns immediately so the reply tool can speak its acknowledgement ("Researching the Mariana Trench now") while the workers run — the main LLM is free to take follow-up turns.

What it adds vs. the prior demos

The other examples use the request/response half of the bus protocol (main LLM → UIWorker → reply). This one adds the streaming job-group half: UIWorker → peer workers → progress events forwarded to the client. The architecture grows from "one delegate" to "one delegate plus a worker pool" — the peers are plain BaseWorkers launched on the runner.

Run

Two terminals.

Terminal 1 — bot:

cd examples/multi-worker/ui-worker/async-tasks
uv run python bot.py

The bot starts on http://localhost:7860.

Terminal 2 — client:

cd examples/multi-worker/ui-worker/async-tasks/client
npm install            # one-time
npm run dev

Open http://localhost:5173 and click Connect.

What to try

The workers are simulated (canned summaries, randomized asyncio.sleep delays) so the demo focuses on the protocol, not the AI. Each research call takes a few seconds.

• "Research the Mariana Trench." — the worker spawns three peers, acknowledges in one short reply, and a card appears showing each peer's status as it progresses (searching → found N results → summarizing → completed).
• "Look up octopus cognition." — same flow; a second card stacks.
• "Research the moon, then research Mars." — two groups run concurrently.
• "How are you?" (no research) — quick reply, no job group.
• Click Cancel on an in-flight card — the cancellation routes through, the peers' tasks raise CancelledError, and their responses come back as cancelled.

Requirements

• OPENAI_API_KEY
• DEEPGRAM_API_KEY
• CARTESIA_API_KEY

A .env in the example folder is the easiest way to set these (see examples/multi-worker/env.example).

What this example doesn't show

Real worker integrations (the peers are simulated), LLM-driven peers (these are pure data-fetch — a peer can itself be an LLMWorker), streaming chunks (send_job_stream_data for progressive output), or worker-to-worker fan-out (nested job groups).