What to build
A working example that creates custom Langflow components for Deepgram STT and TTS, enabling developers to visually build voice agent pipelines in Langflow's drag-and-drop interface — connecting microphone input → Deepgram transcription → LLM processing → Deepgram speech synthesis without writing pipeline code.
Why this matters
Low-code/visual AI builders like Langflow are increasingly popular with teams that want to prototype voice agents without building WebSocket infrastructure from scratch. Langflow has strong LLM integration but no built-in speech capabilities. Deepgram custom components for Langflow would let developers drag-and-drop voice into any Langflow pipeline, dramatically reducing time-to-prototype for voice agent experiments. This also serves teams where the voice agent designer is a product manager or conversation designer rather than a backend engineer.
Suggested scope
- Language: Python
- Deepgram APIs: Pre-recorded STT, Streaming STT, TTS
- Framework: Langflow (custom components)
- Components:
DeepgramSTT Langflow component: accepts audio input, returns transcriptDeepgramTTS Langflow component: accepts text, returns audioDeepgramStreamingSTT component: real-time microphone streaming- Example flow JSON: voice-in → STT → OpenAI/Claude → TTS → voice-out
- README with screenshots of the visual flow and setup instructions
- Complexity: Medium — custom Langflow component classes + example flow export
Acceptance criteria
Raised by the DX intelligence system.
What to build
A working example that creates custom Langflow components for Deepgram STT and TTS, enabling developers to visually build voice agent pipelines in Langflow's drag-and-drop interface — connecting microphone input → Deepgram transcription → LLM processing → Deepgram speech synthesis without writing pipeline code.
Why this matters
Low-code/visual AI builders like Langflow are increasingly popular with teams that want to prototype voice agents without building WebSocket infrastructure from scratch. Langflow has strong LLM integration but no built-in speech capabilities. Deepgram custom components for Langflow would let developers drag-and-drop voice into any Langflow pipeline, dramatically reducing time-to-prototype for voice agent experiments. This also serves teams where the voice agent designer is a product manager or conversation designer rather than a backend engineer.
Suggested scope
DeepgramSTTLangflow component: accepts audio input, returns transcriptDeepgramTTSLangflow component: accepts text, returns audioDeepgramStreamingSTTcomponent: real-time microphone streamingAcceptance criteria
Raised by the DX intelligence system.