Pipecat

Pipecat is an open-source Python framework built by Daily for creating real-time voice and multimodal conversational AI agents. The framework supports fully programmable voice agents and multimodal interactions, providing flexibility for developers building conversational systems.

This guide covers routing telemetry from Pipecat applications to InteractiveAI for monitoring, debugging, and evaluating voice agent behavior.

chevron-rightKey Featureshashtag
  • Hierarchical Tracing: Track complete conversations, individual turns, and service calls

  • Service Tracing: Detailed spans for text-to-speech, speech-to-text, and LLM services with rich context

  • TTFB Metrics: Time To First Byte measurements for latency analysis

  • Usage Statistics: Character counts for text-to-speech and token usage for LLMs

Prerequisites

  • InteractiveAI account with API credentials

  • Service API keys (Deepgram, Cartesia, OpenAI, or alternatives)


Trace Structure

Traces organize hierarchically by conversation and turn:

Conversation (conversation-uuid)
├── turn-1
│   ├── stt_deepgramsttservice
│   ├── llm_openaillmservice
│   └── tts_cartesiattsservice
└── turn-2
    ├── stt_deepgramsttservice
    ├── llm_openaillmservice
    └── tts_cartesiattsservice
    turn-N
    └── ...

This structure enables analysis at the conversation level and turn-by-turn inspection.


Configuration

Encode API Credentials

Base64 encode your InteractiveAI public and secret key:

Environment Variables

Create a .env file with your credentials:


Installation

Use only the HTTP exporter. If conflicts occur, uninstall the gRPC exporter.


Enabling Tracing

Configure OpenTelemetry with the HTTP exporter in your application:


Running the Application


Trace Visibility

InteractiveAI then displays:

  • Complete conversation flows with turn-by-turn breakdown

  • Speech-to-text transcriptions and language detection

  • LLM requests with prompts, completions, and token counts

  • Text-to-speech synthesis with voice configuration and character usage

  • Time To First Byte metrics for latency optimization

  • Service-level performance data and processing durations

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