Positive users hail LangChain's LangSmith tracing for voice agents as a game changer for monitoring, while negative users mock the library's excessive abstraction layers as rendering observability ineffective.
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@hwchase17 "don't fly blind" said the guy whose library has 47 abstraction layers between you and what's actually happening 💀
@LangChain @pipecat_ai @livekit @OpenAI @GeminiApp Adding observability to LangChain is like putting a GPS tracker on a labyrinth 📍
@hwchase17 voice agents finally getting their instrument rating no more IFR in prod
@hwchase17 this is such a game changer for monitoring everything
Voice agents are exploding. Don’t let them be a black box in production. Today, we’re launching LangSmith tracing for 4 voice frameworks: 🎙️ @pipecat_ai 🎙️ @livekit 🎙️ @OpenAI Realtime 🎙️ @GeminiApp Live (Google ADK) Learn more: https://www.langchain.com/blog/trace-voice-agents-in-langsmith
🎧 Full audio on your traces ⚡ STT/TTS latency 🗣️ interruptions + VAD Only a few lines of code to set up👇
native integrations for four leading voice frameworks so you can see whats happening dont fly blind
Positive users hail LangChain's LangSmith tracing for voice agents as a game changer for monitoring, while negative users mock the library's excessive abstraction layers as rendering observability ineffective.
Based on 4 visible X reactions from 10 accounts; directional sample.
Ask a question below.
Published answers will appear here.