This Open-Source Voice AI Framework Actually Works (TEN Framework)
Key Takeaway
TEN Framework provides a graph-based runtime that manages complex, interruptible voice conversations by decoupling voice agent components into independent, modular extensions.
Highlights
TEN Framework organizes voice agents as graphs of independent extensions rather than linear pipelines to handle messy, real-time conversations.
Extensions for STT, LLM, TTS, and VAD can be developed using Python, C++, Go, Rust, or TypeScript.
The framework requires several API keys, specifically Agora for audio, Deepgram for speech recognition, OpenAI for LLMs, and 11Labs for text-to-speech.
Docker Compose enables a complete environment setup for the framework with a single command.
A visual designer tool allows developers to debug complex data flows and identify bottlenecks within the agent's graph architecture.
Timeline
Architecture for Real-Time Conversations
Standard voice pipelines often collapse because they fail to handle concurrent streaming, silences, or user interruptions. By treating components as individual nodes in a graph, the framework allows the agent to cancel generation or stop speaking dynamically during active conversations. This structure effectively separates responsibilities for speech recognition, model processing, and voice output.
- TEN Framework manages voice agents as graphs of decoupled extensions.
- Linear pipelines fail in real-time scenarios because they lack robust handling for user interruptions.
- Extensions enable separate management of STT, LLM, TTS, and VAD components.
- Developers can write extensions in Python, C++, Go, Rust, or TypeScript.
Setup and Deployment
Getting started involves providing specific API keys for the core audio and AI services. Once configured, Docker handles the environment setup. Testing reveals that the framework can detect and react to user interruptions mid-sentence, though agent responses occasionally struggle with topic relevance when not strictly prompted.
- Deployment relies on a Docker Compose command to initialize the runtime environment.
- Initial setup requires configuring API keys for Agora, Deepgram, OpenAI, and 11Labs.
- Ready-made configurations for different voice assistants are available within the framework.
- The framework successfully manages interruptions during live voice testing.
Debugging and Suitability
The visual designer is essential for debugging the complex connections inherent in real-time voice systems. While the setup process is more intensive than linear pipeline tools, it offers significant control for complex agents. It is recommended for production environments, whereas simpler alternatives remain preferable for rapid prototyping.
- The visual designer tool provides a graphical representation of the extension graph and data flow.
- This framework suits production-grade voice agents requiring low latency and multi-modal input.
- Simple text-based agents are better served by lower-overhead alternatives.
- LiveKit Agents or PipeCat are recommended for faster prototyping with less setup friction.
Description
In this video, I check out TEN Framework, an open-source framework for building real-time voice AI agents that can handle streaming audio, interruptions, VAD, turn detection, STT, LLMs, TTS, avatars, telephony, and multimodal AI workflows. Most voice AI agents look good but fail when users interrupt, pause, talk over the assistant, or expect a natural conversation, so I wanted to see if TEN Framework actually solves the painful parts of building production-ready voice agents. We’ll look at how TEN uses graph-based extensions, TMAN Designer, and real-time audio pipelines. 🔗 Relevant Links TEN Repo - https://github.com/ten-framework/ten-framework TEN Docs - https://theten.ai/docs ❤️ More about us Radically better observability stack: https://betterstack.com/ Written tutorials: https://betterstack.com/community/ Example projects: https://github.com/BetterStackHQ 📱 Socials Twitter: https://twitter.com/betterstackhq Instagram: https://www.instagram.com/betterstackhq/ TikTok: https://www.tiktok.com/@betterstack LinkedIn: https://www.linkedin.com/company/betterstack 📌 Chapters: 00:00 Why voice AI agents still feel broken 00:40 What is TEN Framework? 01:38 Why STT → LLM → TTS is not enough 02:32 Running TEN Framework with Interruption Test 04:46 How TEN Framework uses graph-based extensions 04:25 TEN Framework vs Voicebox 05:19 What developers like about TEN Framework 06:30 Setup, Docker, API keys, and learning curve 06:56 Should you use TEN Framework?