Trace voice agents in LangSmith

LangSmith has introduced Python integrations for tracing four popular voice agent frameworks: Pipecat, LiveKit, OpenAI Realtime, and Gemini Live with Google ADK, reflecting the growing practicality and market expansion of voice agents. This development is driven by advancements in voice activity detection, emotive speech models, and large language models (LLMs) capable of real-time dialogue. Like chat-based agents, voice agents require observability to ensure effective operation, necessitating the ability to trace and evaluate interactions within the voice pipeline. LangSmith's new release allows voice and text agents to coexist within the same review and collaboration workflows, offering native support for capturing and tracing voice interactions through architectures like the "sandwich" and the speech-to-speech models. The "sandwich" architecture involves chaining components such as speech-to-text (STT), a text-based agent, and text-to-speech (TTS), while the speech-to-speech architecture utilizes a multi-modal model for direct audio processing. LangSmith's tracing integrations provide full observability into production conversations by capturing metadata, inputs, outputs, latency, and events like interruptions, enabling comprehensive insights into voice interactions.

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