Build a Voice Agent with Daily Bots: Hosted Pipecat Cloud (2026)

TL;DR — Daily Bots is the hosted version of Pipecat. You POST a JSON config to /start, get a Daily room URL back, and connect any browser/iOS/Android client with the RTVI SDK. No bot infrastructure to run yourself.

What you'll build

A 20-line Node script that spins up a Cartesia-voiced GPT-4o bot in a fresh Daily room, plus a 30-line React client that joins it with mic + speaker — all running through Daily's global SFU.

Architecture

flowchart LR
  CL[Browser RTVI client] -- WebRTC --> RM[Daily room]
  AP[Your /start endpoint] -- POST /bots/start --> DB[Daily Bots API]
  DB -- spawns --> BOT[Hosted Pipecat bot]
  BOT -- audio --> RM --> CL

Step 1 — Get keys

Sign up at dashboard.daily.co for a Daily Bots account (separate from the Daily video API). Add your OpenAI + Cartesia keys in the dashboard secrets vault — Daily Bots references them by name, not value.

Step 2 — Server: POST /start

```ts // app/api/start/route.ts export async function POST() { const r = await fetch("https://api.daily.co/v1/bots/start", { method: "POST", headers: { Authorization: Bearer ${process.env.DAILY_API_KEY}, "Content-Type": "application/json", }, body: JSON.stringify({ bot_profile: "voice_2024_10", max_duration: 600, services: { stt: "deepgram", llm: "openai", tts: "cartesia" }, config: [ { service: "vad", options: [{ name: "params", value: { stop_secs: 0.4 } }] }, { service: "tts", options: [{ name: "voice", value: "79a125e8-cd45-4c13-8a67-188112f4dd22" }] }, { service: "llm", options: [ { name: "model", value: "gpt-4o" }, { name: "initial_messages", value: [ { role: "system", content: "You are a friendly clinic concierge." }, ] }, { name: "run_on_config", value: true }, ]}, ], }), }); return Response.json(await r.json()); } ```

Step 3 — Client: RTVI React

```tsx "use client"; import { RTVIClient } from "@pipecat-ai/client-js"; import { DailyTransport } from "@pipecat-ai/daily-transport";

export function VoiceBot() { async function connect() { const { room_url, token } = await fetch("/api/start", { method: "POST" }) .then((r) => r.json()); const client = new RTVIClient({ transport: new DailyTransport(), params: { baseUrl: room_url, token }, enableMic: true, enableCam: false, }); await client.connect(); } return Talk to bot; } ```

Step 4 — Swap the LLM live

POST /bots/<id>/action with {"service":"llm","action":"set_model","arguments":[{"name":"model","value":"claude-3-5-sonnet"}]} and the bot hot-swaps providers mid-session.

Step 5 — Function calls

Add tools to the llm config. When the LLM emits a tool call, Daily Bots forwards it to your webhook URL and resumes once you POST the result back.

Step 6 — Inspect transcripts

Subscribe to the transcript RTVI message type on the client to render live captions, or pull the recording + transcript from the Daily REST API after the call.

Pitfalls

• Bot profile pin: Always pin bot_profile: "voice_2024_10" (or newer dated tag) — "voice_latest" can change overnight and break configs.
• run_on_config: true: Without it, the bot waits silently until the user speaks first — unfriendly for outbound calls.
• Region selection: Pass { "geo": "us-east" } for SIP/PSTN bridging tasks — round-trip latency matters more than for browser-only bots.
• Concurrency limits: Default is 5 concurrent bots — file a support ticket before scaling promos.

How CallSphere does this

Explore a live demo and compare current plans to find the right fit for your business.

FAQ

Pricing? Per-bot-minute, billed against the model providers you choose plus a Daily margin — typically $0.05-0.20/min.

SIP/PSTN? Yes, via Daily's Pinless SIP — POST {"sip": {"display_name": "..."}} to bridge a phone number into the bot's room.

Recording? Set recording_settings: { type: "cloud" } — MP4 + transcript appear in your S3 in ~30s.

Open-source fallback? Run the same Pipecat config on your own infra — same bot code, just self-hosted.

Sources

• Daily Bots Docs - Introduction - https://docs.dailybots.ai/introduction
• Daily Bots Docs - Build Your First Bot - https://docs.dailybots.ai/tutorial/01-setup
• Daily Bots Docs - Client Code - https://docs.dailybots.ai/tutorial/04-client-code
• Daily Bots Demo - https://demo.dailybots.ai/

How this plays out in production

Building on the discussion above in Build a Voice Agent with Daily Bots: Hosted Pipecat Cloud (2026), the place this gets non-obvious in production is the latency budget — every leg of the audio loop (capture, ASR, reasoning, TTS, transport) eats into the <1s response window callers expect. Treat this as a voice-first system from the first prompt: the agent's persona, its tool surface, and its escalation rules all flow from that single decision. Teams that ship fast tend to instrument the loop end-to-end before they tune any single component, because the bottleneck is rarely where intuition puts it.

Voice agent architecture, end to end

A production-grade voice stack at CallSphere stitches Twilio Programmable Voice (PSTN ingress, TwiML, bidirectional Media Streams) to a realtime reasoning layer — typically OpenAI Realtime or ElevenLabs Conversational AI — with sub-second response as a hard SLO. Anything north of one second of perceived silence and callers either repeat themselves or hang up; that single number drives the whole architecture. Server-side VAD with proper barge-in support is non-negotiable, otherwise the agent talks over the caller and the conversation collapses. Streaming TTS with phoneme-aligned interruption keeps the cadence natural even when the user changes their mind mid-sentence. Post-call, every transcript is run through a structured pipeline: sentiment, intent classification, lead score, escalation flag, and a normalized slot extraction (name, callback number, reason, urgency). For healthcare workloads, the BAA-covered storage path, audit logs, encryption-at-rest, and PHI-safe transcript redaction are wired in from day one, not bolted on at compliance review. The end state is a system where every call produces a row of structured data, not just a recording.

FAQ

What changes when you move a voice agent the way Build a Voice Agent with Daily Bots: Hosted Pipecat Cloud (2026) describes?

Treat the architecture in this post as a starting point and instrument it before you tune it. The metrics that matter most early on are end-to-end latency (target < 1s for voice, < 3s for chat), barge-in correctness, tool-call success rate, and post-conversation lead score distribution. Optimize whatever the data flags as the bottleneck, not whatever feels slowest in your head.

Where does this break down for voice agent deployments at scale?

The two failure modes that bite hardest are silent context loss across multi-turn handoffs and tool calls that succeed in dev but get rate-limited in production. Both are solvable with a proper agent backplane that pins state to a session ID, retries with backoff, and writes every tool invocation to an audit log you can replay.

How does the CallSphere healthcare voice agent handle a typical patient intake?

The healthcare stack runs 14 specialist tools against 20+ database tables, captures intent and slots in real time, and produces a post-call sentiment score, lead score, and escalation flag for every conversation — so the front desk inherits a triaged queue, not a stack of voicemails.

See it live

Book a 30-minute working session at calendly.com/sagar-callsphere/callsphere-llc-meeting and bring a real call flow — we will walk it through the live healthcare voice agent at healthcare.callsphere.tech and show you exactly where the production wiring sits.