A wiring kit for voice agents that talk, draw, and dial phones

What it does

TEN provides the infrastructure for real-time multimodal conversational AI agents. It packages a visual designer called TMAN Designer with a menagerie of working examples—voice assistants, a voice-driven doodle board, speaker diarization, lip-sync avatars, SIP calling, transcription, and an ESP32-S3 hardware demo—that wire together speech-to-text, LLMs, and text-to-speech into low-latency pipelines.

The interesting bit

The framework treats an agent as a modular graph of extensions; you can reconfigure STT, LLM, and TTS nodes inside TMAN Designer by right-clicking and punching in new API keys, then ship the result as a Docker container or split it into a long-running backend plus a Vercel or Netlify frontend. The README is essentially a quick-start guide for these examples, so the boundary between core framework capabilities and demo scaffolding is blurry.

Key highlights

• Working examples cover RTC and WebSocket voice assistants, real-time drawing from voice prompts, speaker diarization, and SIP phone calls.
• Avatar integrations include Live2D, Trulience, HeyGen, and Tavus, with MotionSync-powered lip sync.
• TMAN Designer provides a visual, property-panel interface for customizing agent extensions without hand-editing JSON.
• Deployment patterns include all-in-one Docker images or a split architecture with a containerized backend and a separately hosted frontend.
• An ESP32-S3 Korvo V3 example demonstrates embedded hardware integration.

Caveats

• Every example requires third-party API keys—Agora, OpenAI, Deepgram, and ElevenLabs—and the README does not suggest offline or self-hosted model alternatives.
• The development environment needs at least 2 CPU cores and 4 GB RAM, with initial builds taking roughly 5–8 minutes.
• The documentation is heavily weighted toward quick-start steps for the bundled examples; framework internals and extension APIs are largely unexplained.

Verdict

Worth exploring if you need to prototype a real-time voice or multimodal agent fast and do not mind tethering to commercial speech and LLM APIs. Pass if you are looking for a deep, standalone framework reference or need to run entirely offline.