Build a real-time voice agent by connecting a client transport to a streamed pipeline for speech recognition (STT), a language model (LLM), and speech synthesis (TTS). Pipecat orchestrates those components; it does not provide a universal latency guarantee. The official quickstart uses browser audio over WebRTC with Silero VAD, Deepgram STT, OpenAI, and Cartesia TTS, and says its example round trip typically completes in under one second. Treat that as documentation context, not a promise for your deployment.
What you are building
A Pipecat voice agent is a Python application that processes an audio conversation as a real-time pipeline. A client—such as a browser—captures the user’s microphone and plays the assistant’s audio. A server receives audio, identifies speech and turn boundaries, transcribes it, sends the conversation to an LLM, synthesizes the response, then streams audio back.
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Pipecat is an open-source orchestration framework, licensed BSD-2, designed to work with different AI providers and hosting environments. STT, LLM, TTS, transport, and hosting are separate choices that affect the finished system. See the Pipecat documentation.
Start with the documented Python quickstart
The official quickstart requires Python 3.11 or later and the uv package manager. It scaffolds a project with the Pipecat CLI and uses Deepgram, OpenAI, and Cartesia credentials for its example services. Follow the current commands and project setup in the Pipecat Quickstart, since exact CLI steps can change.
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- Install the prerequisites. Use Python 3.11 or newer and install
uvas directed by its official installation instructions. - Create the starter project. Use the Pipecat CLI flow in the quickstart to scaffold the browser voice example.
- Configure provider credentials. Supply the required API keys for Deepgram, OpenAI, and Cartesia through the configuration method shown in the current quickstart. Keep secrets out of source control.
- Run locally and test both directions. Grant the browser microphone permission, speak a short prompt, and confirm that the response is audible. The quickstart notes that first startup may take about 20 seconds while required models download and imports run; this is startup time, not per-turn latency.
The sample pipeline’s main sequence is transport input, STT, user context aggregation, LLM, TTS, transport output, and assistant context aggregation. The context aggregators keep conversation state associated with the recognized user input and generated assistant response.
Choose the right transport before tuning latency
For browser-to-server voice, Pipecat recommends WebRTC. It handles audio timing, jitter buffering, browser echo cancellation, and network changes that a custom WebSocket implementation would otherwise need to address. The framework’s transport guidance says, “For any client-to-server voice application, WebRTC is the right choice.” Read Choosing a Transport before selecting the production setup.
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| Transport | Good fit | Operational trade-off |
|---|---|---|
| SmallWebRTC | Local development, self-hosting, or cases where client and server are nearby and network latency is already low. | Direct media can be a straightforward starting point; you own the deployment and must account for the network conditions your users actually have. |
| Daily | Managed WebRTC routing for production users across locations, devices, or variable networks. | Pipecat’s guide identifies Daily as a managed option and says Pipecat Cloud includes Daily. The page attributes about 75 Points of Presence and approximately 13 ms P50 first-hop latency to Daily’s network; those are vendor-reported network figures, not an end-to-end agent measurement. |
| LiveKit | Production WebRTC applications that need infrastructure or multi-participant features. | Available as self-hosted software or a managed cloud service; the choice changes how much infrastructure your team operates. |
| WebSocket | Server-to-server communication on controlled networks, text-only bots, or telephony media streams delivered by a provider. | Pipecat advises against it for ordinary browser-to-server voice, where WebRTC supplies audio and network handling the application would otherwise need. |
The docs do not provide a neutral, like-for-like latency or cost benchmark across these options. Choose according to client type, user geography, network reliability, required features, and operational ownership—not a single advertised network figure.
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Voice latency is cumulative: turn-end detection, transport, transcription, LLM generation, speech synthesis, and playback all contribute. A streaming pipeline lets a stage begin work as upstream output becomes available instead of waiting for a complete transcript or response. Prefer streaming modes for STT, LLM, and TTS when the chosen integrations support them.
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Pipecat’s overview presents 500–800 ms as a typical voice-interaction range, while its quickstart says its example round trip typically finishes in under one second. Neither page supplies a reproducible benchmark configuration or a guarantee for another provider, region, network, or deployment. For background, see Pipecat’s framework overview and the quickstart.
Measure the delay users experience
- Record the time from the user’s last meaningful speech to the assistant’s first audible audio. This captures perceived responsiveness better than measuring only model generation.
- Also track full-turn completion and, where instrumentation allows, the time spent in turn detection, transport, STT, LLM, TTS, and playback.
- Test in the target deployment region using representative microphones, accents, utterance lengths, and network conditions. A local test does not establish performance for geographically distributed users.
- Compare provider choices using measured latency, streaming behavior, language and voice support, reliability, integration needs, and service cost. The quickstart establishes that providers can be changed, but does not offer comparative provider benchmarks.
Configure speech detection and turn-taking
Voice activity detection (VAD) identifies speech versus silence; it does not reliably determine whether a speaker has finished a thought. Turn strategies use VAD together with transcription signals and turn detection to decide when a user starts or ends a turn. Pipecat’s documented default stop strategy uses Smart Turn; a configurable speech timeout is a simpler alternative. Begin with the documented defaults, then tune against recordings representative of your users rather than shortening silence thresholds by guesswork. See Speech Input and Turn Detection.
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The quickstart uses local Silero VAD, which Pipecat describes as low overhead. The key distinction is that VAD answers “is there speech?” while turn logic answers “is the user done for now?” Aggressive end-of-turn settings can make the agent respond over a pause; overly conservative settings make users wait.
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Pipecat’s documented turn-start configuration enables interruptions by default. When a user barges in, an interruption frame allows processors to cancel in-flight work, TTS to clear queued output, and the transport to flush audio that has not played. The assistant context records what was actually spoken rather than the unplayed remainder of a generated sentence. This prevents both continued playback over the user and a mismatch between what the user heard and what the conversation state says. See Interruption Handling.
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Move from local testing to deployment
The quickstart demonstrates local development followed by deployment with Pipecat Cloud. For a production app, select transport and hosting together: direct SmallWebRTC can suit local or same-region conditions, while managed WebRTC routing may be more appropriate for users distributed across regions or facing degraded networks. LiveKit is another documented production option when self-hosting, managed infrastructure, or multi-participant capabilities fit the application. These are qualitative fit recommendations, not comparative reliability or latency test results.
Before launch, validate microphone permissions and playback in the client, verify that credentials are configured securely, and test barge-in, pauses, and network variation. A USB headset with microphone can be a convenient testing accessory if the computer’s built-in input or speakers are unsuitable, but the quickstart requires no particular headset.
Quick Recap
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