​
Overview

SentryMetrics extends FrameProcessorMetrics to provide performance monitoring integration with Sentry. It tracks Time to First Byte (TTFB) and processing duration metrics for frame processors.

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Installation

To use Sentry metrics, install the Sentry SDK:

pip install "pipecat-ai[sentry]"

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Configuration

Sentry must be initialized in your application before metrics will be collected:

import sentry_sdk

sentry_sdk.init(
    dsn="your-sentry-dsn",
    traces_sample_rate=1.0,
)

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Usage Example

import sentry_sdk

from pipecat.services.openai.llm import OpenAILLMService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.metrics.sentry import SentryMetrics
from pipecat.transports.services.daily import DailyParams, DailyTransport

async def create_metrics_pipeline():
    sentry_sdk.init(
        dsn="your-sentry-dsn",
        traces_sample_rate=1.0,
    )

    transport = DailyTransport(
        room_url,
        token,
        "Chatbot",
        DailyParams(
            audio_out_enabled=True,
            audio_in_enabled=True,
            video_out_enabled=False,
            vad_analyzer=SileroVADAnalyzer(),
            transcription_enabled=True,
        ),
    )

    tts = ElevenLabsTTSService(
        api_key=os.getenv("ELEVENLABS_API_KEY"),
        metrics = SentryMetrics(),
    )

    llm = OpenAILLMService(
        api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o"),
        metrics = SentryMetrics(),
    )

    messages = [
        {
            "role": "system",
            "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
        },
    ]

    context = OpenAILLMContext(messages)
    context_aggregator = llm.create_context_aggregator(context)

    # Use in pipeline
    pipeline = Pipeline([
        transport.input(),
        context_aggregator.user(),
        llm,
        tts,
        transport.output(),
        context_aggregator.assistant(),
    ])

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Transaction Information

Each transaction includes:

  • Operation type (ttfb or processing)
  • Description with processor name
  • Start timestamp
  • End timestamp
  • Unique transaction ID

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Fallback Behavior

If Sentry is not available (not installed or not initialized):

  • Warning logs are generated
  • Metric methods execute without error
  • No data is sent to Sentry

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Notes

  • Requires Sentry SDK to be installed and initialized
  • Thread-safe metric collection
  • Automatic transaction management
  • Supports selective TTFB reporting
  • Integrates with Sentry’s performance monitoring
  • Provides detailed timing information
  • Maintains timing data even when Sentry is unavailable