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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.

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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

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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.

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