Quickstart

Four ways in, from smallest to most complete:

1. Run the server with an example agent. Build brisvox-server, point it at examples/agents/support-en.toml, set three provider keys, and talk to it over the websocket protocol.
2. Write an agent with the Python or TypeScript SDK. The server runs the call (audio, speech recognition, turn taking, LLM, speech synthesis) and connects out to your process, which holds your tools and your application logic.
3. Docker and compose. The container image and deploy/compose.yaml: a websocket agent and a Twilio phone agent from one image.
4. A fully local assistant. sherpa-onnx, Ollama and Kokoro on your own machine, no API key, talked to from a browser page.

What you need in all cases:

• Rust (the toolchain is pinned in rust-toolchain.toml; rustup picks it up) for a source build, or Docker for the image.
• API keys for one provider per stage (none for the local assistant). The examples use Deepgram (speech to text), OpenAI (LLM) and Cartesia (text to speech). Keys are read from the environment, never from a file in the repository, and the server does not read a .env file: export them in the shell (or use --env-file with Docker).
• For the browser example (examples/web-assistant) a server built with the webrtc feature, which also needs a C compiler and cmake.

Without keys you can still run everything that does not call a provider: the deterministic simulations and the benchmark harness run on mock providers (cargo run --release -p brisvox-bench -- v2v --sim, see Benchmark results).