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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, enabling real-time performance monitoring and error tracking for your Pipecat applications.

Sentry Metrics API Reference

Pipecat’s API methods for Sentry metrics integration

Example Implementation

Browse examples using Sentry metrics

Sentry Documentation

Official Sentry Python SDK documentation

Sentry Platform

Access performance monitoring and error tracking

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Installation

To use Sentry analytics services, install the required dependencies: 
pip install "pipecat-ai[sentry]"

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Prerequisites

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Sentry Account Setup

Before using Sentry metrics services, you need:
  1. Sentry Account: Sign up at Sentry Platform
  2. Project Setup: Create a project and obtain your DSN
  3. SDK Initialization: Configure Sentry SDK in your application
  4. Metrics Configuration: Set up performance monitoring and error tracking

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Required Configuration

  • Sentry DSN: Your project’s Data Source Name for authentication
  • Traces Sample Rate: Configure performance monitoring sampling
  • SDK Initialization: Initialize Sentry before using metrics

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Key Features

  • Performance Monitoring: Track TTFB and processing duration metrics
  • Error Tracking: Automatic error capture and reporting
  • Frame Processor Metrics: Monitor individual processor performance
  • Real-time Analytics: Live performance data and alerting

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Configuration

SentryMetrics takes no constructor parameters. It automatically detects whether the Sentry SDK has been initialized and logs a warning if it has not.
You must initialize the Sentry SDK in your application before creating SentryMetrics. The metrics collector checks sentry_sdk.is_initialized() at construction time.

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Usage

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Basic Setup

import sentry_sdk
from pipecat.processors.metrics.sentry import SentryMetrics

# Initialize Sentry SDK first
sentry_sdk.init(
    dsn=os.getenv("SENTRY_DSN"),
    traces_sample_rate=1.0,
)

# Create metrics and assign to a service
sentry = SentryMetrics()
tts = SomeTTSService(
    metrics=sentry,
)

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With Multiple Services

sentry_tts = SentryMetrics()
sentry_llm = SentryMetrics()

tts = SomeTTSService(metrics=sentry_tts)
llm = SomeLLMService(metrics=sentry_llm)

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Notes

  • SDK initialization required: Sentry metrics are silently disabled if sentry_sdk.init() has not been called. A warning is logged in this case.
  • Transaction types: The service creates two types of Sentry transactions: ttfb for time-to-first-byte tracking and processing for frame processing duration.
  • Background processing: Transactions are completed in a background task to avoid blocking the pipeline.
  • Graceful shutdown: On cleanup, the service flushes all pending transactions to Sentry with a 5-second timeout.