> ## Documentation Index
> Fetch the complete documentation index at: https://daily-docs-source-analytics-user-turn-strategies.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 and Pipecat Flows Work Together

**Pipecat** defines the core capabilities of your bot — the pipeline and processors that enable receiving audio, transcribing input, running LLM completions, converting responses to audio, and sending audio back to the user.

**Pipecat Flows** complements Pipecat by providing structure to a conversation, managing context and tools as the conversation progresses from one state to another. This is separate from the core pipeline, allowing you to separate conversation logic from 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

```bash theme={null}
uv add pipecat-ai-flows
```

You'll also need Pipecat with 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-flows">
    Source code, issues, and contributions
  </Card>
</CardGroup>