86c2dd5cfc
Processing metrics were an early addition that predated a clear understanding of what timing measurements matter in real-time pipelines. They were inconsistently implemented across services, often broken, and overlapped with the better-defined TTFB metric. - Remove ProcessingMetricsData class and all start/stop_processing_metrics methods from FrameProcessorMetrics, FrameProcessor, and SentryMetrics - Remove all processing metrics calls from 31 service files (LLM, TTS, STT, image, vision, realtime) - Clean up empty _start_metrics() stubs left in STT services - Remove processing metrics handling from RTVI, metrics log observer, pipeline task initial metrics, and strands agents framework - Update tests and examples Remaining metrics (TTFB, LLM token usage, TTS character usage, text aggregation time) are well-defined and consistently implemented.
Pipecat Examples
This directory contains examples to help you learn how to build with Pipecat.
Getting Started
New to Pipecat? Start here:
- Quickstart - Get your first voice AI bot running in 5 minutes (coming soon)
- Client/Server Web - Learn to build web applications with Pipecat's client SDKs (coming soon)
- Phone Bot with Twilio - Connect your bot to a phone number (coming soon)
Foundational Examples
Single-file examples that introduce core Pipecat concepts one at a time. These examples:
- Build on each other progressively
- Focus on specific features or integrations
- Are used for testing with every Pipecat release
See the Foundational Examples README for the complete list.
More Advanced Examples
Ready to explore complex use cases? Visit pipecat-examples for:
- Production-ready applications
- Multi-platform client implementations
- Telephony integrations
- Multimodal and creative applications
- Deployment and monitoring examples