> ## Documentation Index
> Fetch the complete documentation index at: https://daily-docs-pr-4892.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Pipecat Flows

> Build structured conversations for your voice AI agents.

Pipecat Flows is a framework for building structured conversations in your AI applications. It lets you define conversation paths as a graph of nodes, where each node focuses the LLM on a single task with only the tools it needs.

This approach solves a common problem: monolithic prompts with many tools lead to hallucinations and lower accuracy. Pipecat Flows breaks complex tasks into focused steps with clear, specific instructions.

## When to Use Pipecat Flows

Pipecat Flows is best suited for use cases where:

* **You need precise control** over how a conversation progresses through specific steps
* **Your bot handles complex tasks** that can be broken down into smaller, manageable pieces
* **You want to improve LLM accuracy** by focusing the model on one specific task at a time instead of managing multiple responsibilities simultaneously

## How Pipecat Flows Builds on the Pipeline

A Pipecat **pipeline** provides your bot's core mechanics — receiving audio, transcribing input, running LLM completions, converting responses to audio, and sending audio back to the user.

**Pipecat Flows** builds on that pipeline to structure the conversation, managing context and tools as it moves from one state to the next. This keeps your conversation logic cleanly separated from the pipeline mechanics.

<Warning>
  **Pipecat Flows needs a text LLM that supports function calling** — use a
  cascaded **STT → LLM → TTS** pipeline (OpenAI, Anthropic, Google Gemini, AWS
  Bedrock, or any OpenAI-compatible service).

  **Speech-to-speech (realtime) models aren't supported** — Gemini Live, OpenAI
  Realtime, Ultravox, and AWS Nova Sonic. Flows moves between nodes by rewriting
  the LLM's context and tools mid-session, and these realtime APIs don't yet
  expose the controls to do that (a known limitation, tracked upstream). To get
  a graph-of-nodes structure with a realtime model today, build it yourself with
  function calling instead. See the [supported providers
  table](/api-reference/pipecat-flows/overview#llm-provider-support).
</Warning>

## Installation

Pipecat Flows is included with Pipecat. Install Pipecat with the dependencies for your transport, STT, LLM, and TTS providers:

```bash theme={null}
uv add "pipecat-ai[daily,openai,deepgram,cartesia,silero]"
```

## Visual Flow Editor

The [Pipecat Flows Visual Editor](https://flows.pipecat.ai/) lets you design conversation flows visually and export them as JSON configurations.

## Ready to Build?

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/pipecat-flows/guides/quickstart">
    Build your first conversation flow in minutes
  </Card>

  <Card title="API Reference" icon="book" href="/api-reference/pipecat-flows/overview">
    Complete reference docs and technical details
  </Card>

  <Card title="Examples" icon="code" href="/pipecat-flows/examples">
    Explore real-world examples and use cases
  </Card>

  <Card title="GitHub" icon="github" href="https://github.com/pipecat-ai/pipecat">
    Source code, issues, and contributions
  </Card>
</CardGroup>