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Actions allow you to execute custom functionality at specific points in your conversation flow, giving you precise control over timing and sequencing.

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Action Types

  • pre_actions execute immediately when transitioning to a new node, before the LLM inference begins.
  • post_actions execute after the LLM inference completes and any TTS has finished speaking.

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Built-in Actions

Pipecat Flows includes several ready-to-use actions for common scenarios:

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tts_say

Speak a phrase immediately (useful for “please wait” messages): 
"pre_actions": [
    {
        "type": "tts_say",
        "text": "Please hold while I process your request..."
    }
]

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end_conversation

Gracefully terminate the conversation: 
"post_actions": [
    {
        "type": "end_conversation",
        "text": "Thank you for your time!"
    }
]

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function

Execute a custom function at the specified timing: 
"post_actions": [
    {
        "type": "function",
        "handler": end_conversation_handler
    }
]

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Custom Actions

You can define your own actions to handle specific business logic or integrations. In most cases, consider using a function action first, as it executes at the expected time in the pipeline. Register custom action handlers using register_action(): 
async def notify_slack(action: dict, flow_manager: FlowManager):
    channel = action.get("channel", "#general")
    await slack_client.post_message(channel=channel, text=action["text"])

flow_manager.register_action("notify_slack", notify_slack)
Then use it in your node configuration: 
"pre_actions": [
    {"type": "notify_slack", "channel": "#support", "text": "Session started"}
]
Custom actions give you complete flexibility to execute any functionality your application needs, but require careful timing considerations.

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Action Timing

The execution order ensures predictable behavior:
  1. Pre-actions run first upon node entry (in the order they are defined)
  2. LLM inference processes the node’s messages and functions
  3. TTS speaks the LLM’s response
  4. Post-actions run after TTS completes (in the order they are defined)
This timing guarantees that actions execute in the correct sequence, such as ensuring the bot finishes speaking before ending the conversation. Note that custom actions may not follow this predictable timing, which is another reason to prefer function actions when possible.