How a Pipecat Pipeline Works | Pipecat Tutorial | Voice AI

Описание: A conceptual overview of how Pipecat voice AI pipelines work.

In this technical guide, I cover:

• Frames & Processors - The fundamental building blocks of Pipecat pipelines
• Frame Flow - How data moves through the pipeline during a conversational turn (user speech → bot response → loop)
• Interruption Handling - How the system detects and processes user interruptions using control frames
• Voice Activity Detection (VAD) - How VAD works with smart turn detectors to determine when users have finished speaking
• TTSSpeakFrame - Making the bot say specific messages at specific times (welcome messages, etc.)
• STTMuteFilter - Making the bot uninterruptible during certain phases (like opening greetings)
• Observers - Monitoring frame flow for debugging, logging, and telemetry without modifying the pipeline

Perfect for developers getting started with Pipecat or anyone curious about voice AI orchestration internals.

Bonus: Tips at the end on using AI tools (like Claude Code) to explore the codebase and generate documentation with code examples.

Errata:
1. TTS buffers until sentence boundaries, not word boundaries (I misspoke)
2. The actual STTMuteFilter strategy I was trying to remember the name of is called STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE
3. Event hooks are on the transport not PipelineTask and the one I was trying to remember the name of is on_client_connected
4. The LLM outputs LLMFullResponseStartFrame, then a series of LLMTextFrames, then a LLMFullResponseEndFrame

0:00 Introduction & Overview
00:54 Pipeline fundamentals: frames & processors
02:06 User turn: frame flow
04:33 Assistant turn: frame flow
06:48 Handling interruptions
08:15 VAD & smart turn detector
10:36 Making the bot say specific things
11:30 Uninterruptible bot speech
12:31 Observers
13:45 How to use AI tools to get more out of this video
14:41 Conclusion