TEN is an open-source framework for real-time multimodal conversational AI.

TEN Ecosystem includes TEN Framework, Agent Examples, VAD, Turn Detection and Portal.

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Multi-Purpose Voice Assistant — This low-latency, high-quality real-time assistant supports both RTC and WebSocket connections, and you can extend it with Memory, VAD, Turn Detection, and other extensions.

See the Example code for more details.

Doodler — A doodle board that turns spoken or typed prompts into simple hand-drawn sketches, complete with a crayon palette and real-time drawing.

Example code

Speaker Diarization — Real-time diarization that detects and labels speakers, the Who Likes What game shows an interactive use case.

Example code

Lip Sync Avatars — Works with multiple avatar vendors, the main character features Kei, an anime character with MotionSync-powered lip sync, and also supports realistic avatars from Trulience, HeyGen, and Tavus.

See the Example code for different Live2D characters.

SIP Call — SIP extension that enables phone calls powered by TEN.

Example code

Transcription — A transcription tool that transcribes audio to text.

Example code

ESP32-S3 Korvo V3 — Runs TEN agent example on the Espressif ESP32-S3 Korvo V3 development board to integrate LLM-powered communication with hardware.

See the integration guide for more details.

cd ai_agents
cp ./.env.example ./.env

AGORA_APP_ID=
AGORA_APP_CERTIFICATE=

# Deepgram (required for speech-to-text)
DEEPGRAM_API_KEY=

# OpenAI (required for language model)
OPENAI_API_KEY=

# ElevenLabs (required for text-to-speech)
ELEVENLABS_TTS_KEY=

docker compose up -d

docker exec -it ten_agent_dev bash

Check the agents/examples folder for additional samples. Start with one of these defaults:

# use the chained voice assistant
cd agents/examples/voice-assistant

# or use the speech-to-speech voice assistant in real time
cd agents/examples/voice-assistant-realtime

Run task build if you changed any local source code. This step is required for compiled languages (for example, TypeScript or Go) and not needed for Python.

task install
task run

Once the agent example is running, you can access the following interfaces:

localhost:49483	localhost:3000

• TMAN Designer: localhost:49483
• Agent Examples UI: localhost:3000

1. Open localhost:49483.
2. Right-click the STT, LLM, and TTS extensions.
3. Open their properties and enter the corresponding API keys.
4. Submit your changes, now you can see the updated Agent Example in localhost:3000.

TEN also provides a transcriber app that you can run from TEN Manager without using Docker.

Check the quick start guide for more details.

GitHub offers free Codespaces for each repository. You can run Agent Examples in Codespaces without using Docker. Codespaces typically start faster than local Docker environments.

Check out this guide for more details.

Once you have customized your agent (either by using the TMAN Designer or editing property.json directly), you can deploy it by creating a release Docker image for your service.

Note: The following commands need to be executed outside of any Docker container.

cd ai_agents
docker build -f agents/examples/<example-name>/Dockerfile -t example-app .

docker run --rm -it --env-file .env -p 3000:3000 example-app

You can split the deployment into two pieces when you want to host TEN on providers such as Vercel or Netlify.

1. Run the TEN backend on any container-friendly platform (a VM with Docker, Fly.io, Render, ECS, Cloud Run, or similar). Use the example Docker image without modifying it and expose port 8080 from that service.

2. Deploy only the frontend to Vercel or Netlify. Point the project root to ai_agents/agents/examples/<example>/frontend, run pnpm install (or bun install) followed by pnpm build (or bun run build), and keep the default .next output directory.

3. Configure environment variables in your hosting dashboard so that AGENT_SERVER_URL points to the backend URL, and add any NEXT_PUBLIC_* keys the UI needs (for example, Agora credentials you surface to the browser).

4. Ensure your backend accepts requests from the frontend origin — via open CORS or by using the built-in proxy middleware.

With this setup, the backend handles long-running worker processes, while the hosted frontend simply forwards API traffic to it.

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Project	Preview
️TEN Framework Open-source framework for conversational AI Agents.
TEN VAD Low-latency, lightweight and high-performance streaming voice activity detector (VAD).
️ TEN Turn Detection TEN Turn Detection enables full-duplex dialogue communication.
TEN Agent Examples Usecases powered by TEN.
TEN Portal The official site of the TEN Framework with documentation and a blog.

TEN Framework is available on these AI-powered Q&A platforms. They can help you find answers quickly and accurately in multiple languages, covering everything from basic setup to advanced implementation details.

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We welcome all forms of open-source collaboration! Whether you're fixing bugs, adding features, improving documentation, or sharing ideas, your contributions help advance personalized AI tools. Check out our GitHub Issues and Projects to find ways to contribute and show your skills. Together, we can build something amazing!

Contributions are welcome! Please read the contribution guidelines first.

1. The entire TEN framework (except for the folders explicitly listed below) is released pursuant the Apache License, Version 2.0, with additional restrictions. For details, please refer to the LICENSE file located in the root directory of the TEN framework.

2. The components within the packages directory are released under the Apache License, Version 2.0. For details, please refer to the LICENSE file located in each package's root directory.

3. The third-party libraries used by the TEN framework are listed and described in detail. For more information, please refer to the third_party folder.