TEN Framework Review
OSSOpen-source real-time multimodal conversational AI framework (voice + video + avatar) with its own VAD and full-duplex turn-detection models, backed by Agora.
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What we know about TEN Framework
TEN Framework sits in the voice ai category, where it is open-source real-time multimodal conversational AI framework (voice + video + avatar) with its own VAD and full-duplex turn-detection models, backed by Agora. We keep this TEN Framework profile grounded in primary sources, each fact dated to when we last confirmed it.
TEN Framework does not publish a public per-minute rate we have been able to verify, so the price figure here stays blank until we can confirm one. We would rather show nothing than a guessed number; if you have a current TEN Framework quote, it is the fastest way for us to close that gap.
On capabilities, TEN Framework covers native sip trunking, bring-your-own llm, bring-your-own tts voice, interruption handling (barge-in), self-host / on-prem option, and no-code agent builder. Each of those is verified against TEN Framework's own docs or dashboard, not marketing copy.
In total we track 8 verified facts for TEN Framework today, each linking the primary source it came from so you can check our work - and vendor claims we have not measured ourselves are labeled as such on the TEN Framework fact sheet below.
Reviewed by vsref Editorialfacts verified Jul 30, 2026Methodology →
Fact sheet
Every row independently verifiedConsidering a switch? Best TEN Framework alternatives →
TTS in this stack
TEN Framework lets you bring your own text-to-speech voice, so the voice engine is a separate pricing and quality decision. Compare the engines builders plug in most: ElevenLabs, Cartesia, OpenAI TTS, or the full text-to-speech comparison →
STT in this stack
A voice agent hears through its speech-to-text engine, and transcription accuracy and streaming latency are priced per audio minute. Compare the engines builders pair with TEN Framework: Deepgram, AssemblyAI, GPT-4o Transcribe, or the full speech-to-text comparison →