Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.pipecat.ai/llms.txt

Use this file to discover all available pages before exploring further.

Pipecat is an open source Python framework for building voice and multimodal AI agents. It orchestrates AI services, network transports, and audio processing to enable ultra-low latency conversations that feel natural and responsive.

Quickstart

Want to dive right in? Build and run your first Pipecat application

​
What You Can Build

Voice Assistants

Natural, real-time conversations with AI using speech recognition and synthesis

Phone Agents

Connect to your agent via phone for support, intake, and customer service interactions

Multimodal Apps

Applications that combine voice, video, images, and text for rich interactions

Creative Experiences

Storytelling experiences and social companions that engage users

Interactive Games

Voice-controlled games and interactive experiences with real-time AI responses

Conversation Flows

Build structured conversations with Pipecat Flows to complete tasks and improve LLM accuracy

​
How It Works

Pipecat orchestrates AI services in a pipeline, which is a series of processors that handle real-time audio, text, and video frames with ultra-low latency. Here’s what happens in a typical voice conversation:
  1. Transport receives audio from the user (browser, phone, etc.)
  2. Speech Recognition converts speech to text in real-time
  3. LLM generates intelligent responses based on context
  4. Speech Synthesis converts responses back to natural speech
  5. Transport streams audio back to the user
In most cases, the entire round-trip interaction happens between 500-800ms, creating a natural conversation experience for the user. Pipecat Overview

​
A Multi-Agent System

A typical bot is a single agent, but Pipecat is a multi-agent system: you can coordinate many agents that talk to each other over a shared message bus. The same WorkerRunner you use for one bot coordinates many agents, so any Pipecat app is multi-agent ready.
  • The WorkerRunner owns the bus and manages every agent’s lifecycle and discovery.
  • Each agent is a worker. BaseWorker is the foundation and coordinates purely over the bus with no pipeline of its own; PipelineWorker and LLMWorker add a Pipecat pipeline.
  • Any agent can start other agents at runtime: a worker adds child workers (or the runner does), and they immediately join the shared bus.
  • Agents coordinate in more than one way. Handoff transfers control of the conversation to another agent, while jobs and job groups dispatch work to one or many agents in parallel and collect their results.
Agents can run together in one process over an in-process bus, or across separate processes and machines over a Redis or Postgres bus, without changing your agent code.

Build a multi-agent system

Learn how agents, the runner, and the bus work together

​
Ready to Build?

Quickstart

Build and run your first Pipecat application

Core Concepts

Learn about pipelines, processors, transports, and context management

Supported Services

Browse the complete list of 100+ AI service integrations

Deploy

Deploy to Pipecat Cloud or self-host on your own infrastructure